Digital Technology and AI for Smart Sustainable Cities in the Global South: A Critical Review of Literature and Case Studies

Abstract

Many countries across the Global South strive to align their urban development with sustainability goals. Consequently, the notion of smart sustainable cities has emerged, integrating the ideas of smart cities and sustainability. The region faces diverse challenges, including rapid population growth and financial constraints. Infrastructural deficiencies, especially in digital infrastructure and AI adoption, add to these challenges. Therefore, exploring digital technologies and AI is essential for developing smart, sustainable cities in the Global South. This paper examined both the potential and barriers to digital technologies and AI. It also explored policy implications and proposes a framework for smart sustainable cities. A qualitative methodological approach is used, including a systematic literature review and case studies. The study demonstrates how various urban challenges can be addressed with digital technologies and AI, alongside the barriers to their adoption. The study proposes a conceptual framework with three key pillars: adopting digital technologies and AI as the pivotal element, overcoming barriers, and identifying application areas to transform cities into smart sustainable cities. Moreover, the paper discusses policy implications and suggests future directions for research.

1. Introduction

The evolution of the academic and political discourse on sustainable development has progressed from an initial focus on environmental issues to encompass a broader emphasis on the environmental, social, and economic dimensions [1]. This shift gave rise to the concept of sustainable cities as a proactive response to rising issues such as environmental degradation and the need for resilient urban spaces [2]. Recognising this, the United Nations incorporated the development of sustainable cities and communities as a key focus within its Sustainable Development Goals (SDGs). Sustainable cities are characterised by their triple-bottom-line approach, integrating social, economic, and environmental considerations [3,4]. These cities aim to ensure that current and future generations enjoy a high quality of life while fostering resilience and inclusivity [3,5,6,7]. In this context, United Nations SDG 11—Sustainable Cities and Communities underscores its commitment to achieving green, social, and economic sustainability, emphasising inclusivity, safety, resilience, and sustainability. Essentially, sustainable cities prioritise economic opportunities, affordable housing, and robust infrastructure such as public transportation and green spaces, all shaped by participatory urban planning [3,5,6,7,8]. Furthermore, they aim to minimise resource consumption, waste, and emissions, thus promoting sustainable economic growth. This philosophy has been the cornerstone of the development of cities across the world, including many cities in various countries in the Global South.

Alongside the sustainable cities movement, the concept of smart cities has emerged, leveraging technology to enhance efficiency and optimise city functions and services [9,10,11,12,13]. Although there is no universally agreed-upon definition of smart cities, and their characteristics are context-specific, they generally emphasise the integration of economy, governance, infrastructure, and service delivery through advanced digital technology and intelligent systems. The goal is to enhance economic activities, drive social development, and improve energy efficiency, mobility solutions, and waste management, among other objectives. [9,13,14,15,16,17]. In other words, smart cities are characterised by attributes such as smart economy, mobility, environment, governance, and living, and promoting economic efficiency, environmental sustainability, inclusive governance, and liveability [18,19,20,21,22,23].

While sustainable cities traditionally focus on holistic urban development, smart cities introduce a technologically driven approach to urban management [9,13,24,25]. This shift has sparked discussions on integrating sustainability into smart city frameworks to address broader developmental goals [26,27,28,29], despite the criticism that smart cities risk prioritising technological solutions over social and environmental aspects [28,30,31,32,33].

However, there is a growing movement toward the notion of smart sustainable cities, which evolved from integrating smart and sustainable city paradigms. This evolution reflects a dynamic shift toward combining innovative technologies with sustainability goals to create urban environments that are smart and sustainable. In essence, smart sustainable cities leverage advanced digital technologies and artificial intelligence (AI) to optimise systems and processes, with the aim of fostering more liveable, resilient, and sustainable urban environments [19,24,34,35]. For example, smart technologies such as the Internet of Things (IoT), big data analytics, and AI are argued to be vital for developing efficient urban systems because these technologies facilitate the real-time monitoring and management of urban resources, mobility, infrastructure, and services, leading to improved sustainability and resilience [19,36,37,38]. Essentially, economic prosperity, environmental sustainability, the efficient operation of infrastructure, and service delivery ensure enhanced quality of life for the people [9,16,23]. While the adoption and implementation of advanced digital technologies and AI in the cities of the Global North are progressing significantly, it is observed to be challenging in the Global South [39,40,41,42,43].

1.1. The Context of Global South

The “Global South” is a socio-political term that refers to diverse nations primarily in Asia, Africa, Latin America, and the Caribbean, with some definitions also including Oceania. However, countries from these regions, such as Israel, Japan, Taiwan, South Korea, Australia, and New Zealand, are excluded from the Global South. Unlike earlier categorisations such as “developing” or “Third World”, the term highlights shared histories of colonialism, persistent economic inequalities, and structural power imbalances in global geopolitics [44,45].

A key characteristic of the Global South is rapid urbanisation, often accompanied by the expansion of informal settlements and heightened vulnerability to climate change [46]. Cities in these regions face complex challenges, including fast-growing populations, large informal economies, social and economic inequalities, and environmental degradation. Climate change disproportionately affects the Global South due to a combination of factors, such as high exposure to extreme weather events—including tropical storms, droughts, and rising sea levels—along with socio-economic disparities and limited adaptive capacities [47]. Geographic conditions, particularly the dominance of tropical and subtropical climates, further exacerbate these vulnerabilities.

Additionally, large segments of the population in the Global South lack access to fundamental municipal infrastructure and services, constraining economic mobility and perpetuating poverty [48]. Despite these substantial challenges, cities in the Global South are increasingly adopting “smart sustainable city” initiatives, utilising technology and innovative governance strategies to enhance infrastructure, improve public services, foster sustainable economic growth, and promote equitable urban development.

Nevertheless, several cities in the Global South are making strides towards becoming smart sustainable cities. For example, initiatives in countries like India, Kenya, Rwanda, and Brazil are leveraging digital technologies and AI to tackle urban challenges and enhance the quality of life for their people [43,49,50]. These efforts accentuate the potential of digital technologies and AI to drive sustainable urban development and address the socio-economic disparities in these regions.

1.2. The Research Gap, Objectives, and Research Questions

Several studies have examined the concept of smart sustainable cities and the role of digital technology and AI in their development and management. However, much of this research tends to be conceptual in nature, contextually limited to countries in the Global North, or narrowly focused on specific aspects such as infrastructure, service delivery, or governance. For instance, Höjer and Wangel (2015) [28] offered the definitions and challenges of smart sustainable cities. Freeman (2017) [26] explored the origins and application of the smart sustainable city concept, while Trindade et al. (2017) [51] provided a theoretical framework linking sustainable urban development with smart city initiatives. Ahvenniemi et al. (2017) [18] compared smart and sustainable cities, underscoring that smart city technologies should support sustainable development goals. Martin et al. (2018) [33] examined the tensions within smart sustainable city visions and practices, promoting empowerment and inclusivity. Ibrahim et al. (2018) [29] outlined a roadmap for transforming cities into smart, sustainable cities. Bibri’s extensive work (2018a, 2018b, 2019) [5,30,52] investigated future smart sustainable cities and their viability in the era of big data, while Krogstie (2019) [31] introduced an innovative model for future cities. Additionally, Bibri and Krogstie (2019) [31] and Bibri (2021) [53] examined data-driven approaches to advancing smart, sustainable cities. Rjab et al. (2023) [54] critically reviewed the literature to identify barriers to adopting AI in cities. Mutambik (2024) [35] assessed culturally informed technology and its importance in transitioning to smart sustainable cities. Das (2024) [24] discussed how digital technologies, including AI, can serve as catalysts for fostering a synergetic linkage among infrastructure, service delivery, and governance.

However, since the drive for smart sustainable cities in the Global South is relatively recent, research on how digital technology and AI can leverage the development and management of such cities in this context remains limited, emphasising a significant knowledge gap in this domain. Therefore, the objective of this paper is to explore how digital technology and AI can be leveraged to develop smart sustainable cities in the Global South. Specifically, it identified key technologies and AI driving smart sustainable cities, examined their applications in urban management, analysed critical barriers to implementing digital technology and AI, examined successful case studies, and discussed the policy implications for fostering smart sustainable cities in the Global South.

In this context, the research questions investigated are as follows:

• RQ1: How would digital technology and AI address urban challenges to foster smart sustainable cities in the Global South?
• RQ2: What are the barriers to implementing digital technology and AI for smart sustainable cities in the Global South?
• RQ3: What relevant policy implications would enable the creation of smart sustainable cities in the Global South?

2. Conceptualisation of Smart Sustainable Cities and Its Relevance in Global South

2.1. Defining and Conceptualising Smart Sustainable Cities

Smart sustainable cities integrate the principles of smart cities—such as efficient infrastructure, advanced technologies, and smart governance—with sustainability goals such as reducing emissions, conserving resources, and promoting social equity [5,24,31,33]. They integrate technology and data to enhance urban living, promote economic development, and ensure environmental sustainability [24,26,55,56]. In other words, smart sustainable cities leverage ICT and AI to enhance the quality of life across economic, social, environmental, and cultural facets [18]. They aim to attain a dynamic balance between economic development and environmental sustainability while enhancing economic competitiveness, fostering social inclusion, and improving living conditions. This integration involves using advanced digital technology and AI to manage urban operations and services, enhance competitiveness, and improve the quality of life of people [24,26,31,55,56].

Figure 1 presents a conceptual framework of smart sustainable cities, integrating digital technology and AI with key sustainability dimensions—economy, socio-cultural aspects, environment, and governance—alongside smart infrastructure and services to enable efficient urban management, enhanced economic competitiveness, and improved quality of life. It aims at resource conservation and its efficient utilisation, the effective management of urban operations and service delivery, emissions reduction for a healthier environment, smart governance, and social equity and inclusivity. In other words, the intended outcomes of smart sustainable cities encompass efficient urban operations and services management, improved economic competitiveness, and an enhanced quality of life [24,29]. Digital transformation (ICT and AI) is pivotal within this framework.

2.2. Digital Technologies, AI, and Their Systemic Roles in Cities

Different digital technologies, such as ICT and AI, and their applications are well-documented in scholarly literature and reports. Based on this evidence,

Table 1. Digital and AI technologies for smart sustainable cities.

Digital Technologies	Sources	AI Technologies	Sources
ICT	[57,58,59,60,61,62]	Machine Learning (ML)	[34,57,58,59,60,63]
IoT		Deep Learning (DL)
Big Data and Analytics		Natural Language Processing (NLP)
Smart Mobility Solutions		Computer Vision
		Reinforcement Learning
		Robotic Process Automation (RPA)

Compiled from Sources: Kotecha (2023) [34]; European Commission (2013) [57]; European Commission, Joint Research Centre (2021) [58]; Heng, Tsilionis, Scharff, Wautelet (2022) [59]; Khlie, Benmamoun (2024) [60]; Moriarty (2023) [61]; Mortaheb, Jankowski (2023) [62]; and Singh, (2023) [63].

presents digital technologies, including ICT and AI, which can be utilised to develop smart sustainable cities [57,58,59,60]. For instance, digital technologies such as ICT, IoT, big data and analytics, and smart mobility solutions are increasingly available and are transforming urban environments [61,62]. In addition, in recent years, AI technologies such as machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, reinforcement learning, and robotic process automation (RPA) have become available and can make substantial contributions to urban transformation [34,60,63].

2.3. Theoretical Framework for Digital Technologies and AI in Cities

The integration and adoption of digital technology and AI for smart, sustainable cities can be effectively analysed through established theoretical frameworks, such as the Technology–Organisation–Environment (TOE) framework and Innovation Diffusion Theory (IDT). The TOE framework posits that technology adoption within organisations is influenced by three key contexts: the technological context, organisational context, and environmental context. The technological context encompasses the characteristics of the technology itself, including the relative advantage, complexity, compatibility, trialability, and observability [64,65]. The organisational context considers internal factors such as the organisational size and structure, readiness, available resources, top management support, and organisational culture [66,67]. The environmental context includes external pressures like the industry competition, regulatory landscape, market conditions, and existing technological infrastructure [66,68,69]. According to the TOE framework, the likelihood of adoption is enhanced when the technology offers demonstrable advantages, presents manageable complexity, and integrates seamlessly with current systems. It is also enhanced when the organisation possesses the requisite resources and internal support, while external environmental factors also exert significant influence.

IDT explains the diffusion of innovations within a population over time [70]. IDT categorises adopters into five groups—innovators, early adopters, early majority, late majority, and laggards—based on their propensity to adopt new ideas [70]. The diffusion process typically unfolds through the knowledge, persuasion, decision, implementation, and confirmation stages. Key factors influencing diffusion include the innovation’s characteristics (relative advantage, compatibility, complexity, trialability, and observability), adopter characteristics, communication channels employed, and social system characteristics [70,71]. While IDT offers valuable insights, it has been critiqued for its potential pro-innovation bias. It does not fully account for the resistance to change due to cultural or psychological factors and its use of somewhat simplified adopter categories [72].

Given the specific focus of this study on digital technology and AI adoption in smart sustainable cities, the TOE framework is deemed more appropriate. Unlike IDT, which primarily emphasises the social and behavioural aspects of diffusion, TOE provides a structured, multi-dimensional approach that accounts for internal organisational capabilities and external environmental factors—critical considerations in urban technology deployment.

2.4. Relevance to the Global South and Potential Specific Applications

The Global South includes diverse regions in Africa, Asia, and Latin America, which face unique and significant challenges, such as rapid urbanisation, infrastructure deficits, a limited access to technology, governance and institutional challenges, financial constraints, climate-change-induced disasters, and policy issues [73,74,75]. Many countries across the Global South have initiated measures to make their cities sustainable and aspire to make them smart. However, in recent years, with the advent of smart sustainable cities, it has been argued that smart sustainable cities offer a transformative framework to tackle the challenges faced by the cities by fostering innovation, resilience, and inclusive growth [5,24,29,31]. For instance, certain cities in the Global South, e.g., Kigali, Rwanda, have pursued digital transformation through initiatives like a digital land registry to modernise and secure land ownership records [76]. Similarly, India’s Aadhaar program [77] integrates advanced technologies into urban and social services, promoting greater efficiency and sustainability. These initiatives demonstrate how smart sustainable city frameworks can be adapted to address the specific challenges and opportunities unique to different regions within the Global South.

Moreover, smart sustainable cities embody a paradigm shift by integrating sustainability goals with innovative smart solutions for holistic urban development. Given the numerous challenges faced by cities in the Global South, alongside ongoing efforts to promote sustainability and implement smart solutions, coupled with the increasing penetration of digital technologies and AI, this concept is considered particularly relevant to their urban development context. In this context, as shown in

Table 2. Specific application areas of digital technology and AI under different urban domains.

Urban Domains	Application Areas	Sources
Planning and management	Urban planning and management	[16,26,62,78]
Economy	Optimal resource utilisation and management	[18,21,23]
	Knowledge economy	[79]
	Finance	[80]
	Enhance competitiveness	[21,23,79]
Infrastructure and mobility	Transportation and mobility	[37,81,82,83]
	Energy management	[84,85,86]
	Waste management	[5,30,52,87,88,89]
Environment	Environmental monitoring	[90,91,92,93,94]
	Reduction in emissions	[18]
Governance	Governance, participation and responsiveness	[24,81,82,83,84,85,86]
	Equity and inclusiveness	[24,95,96,97,98,99,100]
People and living conditions	Quality of life	[13,22,23,101]
	Health care	[13,22,23]
	Education	[13,22,23]

, digital technology and AI have diverse applications across various urban domains, including but not limited to planning and management, economy, infrastructure and mobility, environment, governance, and people’s living conditions.

3. Methodological Approach

This research employs a qualitative methodological framework to explore the potential and challenges of digital technology and AI for developing smart sustainable cities in the Global South. The approach constitutes an extensive and systematic review of existing literature and case study analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to document the review process and report the literature [102,103].

3.1. Literature Review Process and Reporting

3.1.1. The PRISMA Process

PRISMA is a systematic and structured method commonly employed for reporting systematic literature reviews and meta-analyses conducted aimed at maintaining transparency in research processes. The PRISMA framework consists of four main stages: identification, screening, eligibility, and inclusion. In the identification stage, researchers expand keywords to capture a wide range of relevant articles from databases. Screening is the next stage, where articles are either included or excluded according to set criteria, filtering out irrelevant studies. The eligibility stage involves a detailed review of each article’s title, abstract, methods, results, and discussion sections to confirm that they meet the study’s inclusion criteria and research objectives. Finally, in the inclusion stage, only those articles that fully satisfy the requirements are selected for further analysis [102,103].

3.1.2. The Search Strategy

A comprehensive review of scholarly literature was conducted to examine the key themes related to digital and AI technologies that are relevant to smart cities, sustainable cities, and smart sustainable cities. Given the interdisciplinary nature of the field, the reviewed sources span a wide array of scholarly works, including journal articles, books, book chapters, conference proceedings, reports, news articles, and online content. The search strategy aimed to capture the complexities of digital and AI technology applications for creating smart sustainable cities. For this purpose, three important and large databases, Scopus, Web of Science, and Google Scholar, were searched using search strings developed using several relevant keywords. While searching the databases, the literature review focused on core aspects such as digital technology, AI, and smart sustainable cities, with particular emphasis on the context of the Global South. PRISMA checklist—covering sections like title and abstract, introduction, methods, results, discussion, and funding—was diligently followed throughout the search process.

Identification

The identification process involved searching and listing keywords and their synonyms to retrieve as many articles as possible from different databases. The search included a broad array of keywords, focusing on digital technology and AI in smart sustainable cities. The keywords were strategically chosen to cover a wide range of relevant topics. These included the following: sustainable cities, smart cities, smart sustainable cities, ICT, digital technology, AI technology, application of digital and AI technologies, Global South, potential and challenges of digital and AI technology, strategies for smart sustainable cities, etc. The co-occurrence of keywords was also considered. Search strings were generated in the relevant formats accepted by different databases such as Scopus, Web of Sciences, Google Scholar, etc.

Table 3. The search string.

Database

Search String

Scopus

TITLE-ABS-KEY (Smart sustainable cities AND smart cities AND sustainable cities) AND (Digital Technology OR Information Communication Technology OR Internet of Things OR Big Data and Analytics OR Smart Mobility Solutions) OR (Artificial Intelligence OR (Machine Learning (ML) OR Deep Learning (DL) OR Natural Language Processing (NLP) OR Computer Vision OR Reinforcement Learning OR Robotic Process Automation (RPA)) AND (Potential AND Barriers) AND Strategies AND Global South

provides an example of the search strings used for the study (to search the Scopus database). Figure 2 presents a snapshot of the advanced search process in the Scopus database and the number of articles generated.

The search yielded a total of 6079 articles from three databases and 36 reports from websites. Duplicate records and irrelevant articles were excluded, resulting in 4648 articles and 36 reports for the next screening step.

Screening

The second phase of the search process involved screening and applying defined inclusion and exclusion criteria for precision.

Table 4. Inclusion and exclusion criteria.

Inclusion	Exclusion
Peer-reviewed journals, books, book chapters, conference proceedings, and reports	Non-peer-reviewed journals
Empirical and conceptual papers	Review papers, patents, laws, meta-analyses, opinion articles, and debate articles
English language	Other languages than English
Smart cities, sustainable cities, and smart sustainable cities	Studies focusing on hardware and software of technology and AI
Digital technology, Information Communication Technology, Internet of Things, Artificial Intelligence, Machine Learning, Robotics, Deep Learning (DL), Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, Robotic Process Automation (RPA)	Studies not focusing on smart, sustainable, and smart sustainable cities.
Global South, developing countries

outlines these criteria, which were designed using the Population, Intervention, Comparator, Outcomes, Study Characteristics, and Other (PICOSO) framework. The keywords and search strings used in the literature search were primarily aligned with these inclusion criteria.

Inclusion criteria comprised empirical and review articles published in peer-reviewed journals, conference proceedings articles, books, book chapters, reports, and relevant web and newspaper articles. The studies focused on contexts that include smart cities, sustainable cities, and smart sustainable cities in the Global South or developing countries, with emphasis on digital technology, ICT, IoT, AI, machine learning, robotics, deep learning (DL), natural language processing (NLP), computer vision, reinforcement learning, and robotic process automation (RPA).

In contrast, exclusion criteria were applied strategically to refine the search scope. Non-peer-reviewed articles, patents, laws, treaties, and publications in languages other than English were excluded. Studies that are not focused on smart, sustainable, or smart sustainable cities, as well as those centred primarily on technical aspects of hardware, software, and AI, were also omitted. At this stage, 143 reports from databases and 24 reports from other sources (such as websites/reports), were assessed for eligibility. The timeline for publications was set to include studies up to October 2024.

Data Extraction and Quality Appraisal

Data were extracted using a standardised extraction form that documented details such as authorship, publication date, research aims and objectives, methodology, key findings, contributions, recommendations for future research, and study limitations. The extracted information was organised in an Excel spreadsheet. The Cochrane Risk of Bias (RoB 2) tool for randomised trials was utilised to evaluate potential bias. The assessments were conducted by the researcher and another independent assessor, with one verifying the other’s evaluations, followed by an independent review for accuracy. The majority of the studies were found to have a low risk of bias.

The PRISMA checklist, characteristics of the included studies, and the summary RoB 2 analysis results are included in Supplementary File S1, Supplementary File S2, and Supplementary File S3, respectively. The bias-related findings, such as Summary Risk of Bias analysis and Intention of Threat analysis, and individual Intention to Threat analysis are presented in Supplementary Files S4 and S5, respectively.

Eligibility and Inclusion in the Study

In the third phase, the eligibility assessment involved thoroughly reviewing each article’s title, abstract, objectives, methods, results, and discussion to verify alignment with the study’s inclusion criteria and central focus. Articles were excluded if they (1) failed to contribute meaningfully to the study’s aims or did not clearly align with the research objectives, (2) demonstrated potential bias through selective reporting or lacked substantial, direct contributions, or (3) were qualitative observational studies without robust evidence. Blogs and opinion pieces without supporting evidence were also excluded from the report selection.

3.2. Case Study Contexts

To reinforce the findings of the literature, case study analyses were conducted on adopting digital technology and AI in four Global South countries: India, Rwanda, Kenya, and Brazil. Selection criteria included urbanisation levels, smart city initiatives, and digital technology and AI adoption efforts. These countries are rapidly urbanising, actively pursuing smart and sustainable city initiatives, leading in digital technology adoption, and operating under democratic governance.

For example, Nairobi (Kenya), Kigali (Rwanda), and Brazil’s cities—São Paulo, Rio de Janeiro, and Curitiba—are at the forefront of smart city development, while India’s Smart Cities Mission aims to transform 100 cities into smart and sustainable urban centres. Although specific city-level cases were not analysed in detail, the impact of digital technology and AI across various sectors in these countries underscores their potential to drive urban transformation.

3.3. Analysis and Synthesis

3.3.1. Record Search Analysis

The records search was analysed using descriptive statistics such as the number of records identified, duplicates removed, and records screed, retrieved, and assessed following the PRISMA process; moreover, the numbers and share of articles included in the study were also observed.

3.3.2. Bibliographic Data Analysis

A comprehensive bibliometric analysis was performed on the collected articles using VOSviewer software. The analysis included network visualisations of author keyword co-occurrences and their density to identify research trends and dominant focus areas. Additionally, co-author–country and citation–country network analyses were carried out to highlight the primary regions contributing to research on smart sustainable cities and assess Global South countries’ representation in this field. Furthermore, bibliographic coupling with source network analysis was conducted to identify the key publication sources within this domain.

3.3.3. Thematic Analyses

The selected literature underwent a systematic review followed by a thematic analysis. This analysis was organised using an analytical framework with central themes and subthemes (

Table 5. Themes and subthemes for thematic analyses.

Sl No.	Themes	Subthemes
1	Smart Sustainable Cities	Defining Smart, Sustainable Cities Conceptualising Smart Sustainable Cities Relevance to the Global South
2	Digital and AI technologies for driving Smart Sustainable Cities
3	The Potential of Digital and AI Technologies in Smart, Sustainable Cities	The potential of digital technologies in smart sustainable cities The potential of AI technologies in smart sustainable cities
4	Barriers to adoption and implementation	Infrastructure Deficits Digital Divide and inequality Policy and Governance Financial Constraints
5	Specific applications of digital and AI technologies for smart sustainable cities in the Global South	Urban Planning and Management Transportation and Mobility Energy Management Waste Management Environmental Monitoring Governance and Participation
6	Case Studies	India Rwanda Kenya Brazil

). These themes emerged from the study characteristics of the literature reviewed and the study’s scope, ensuring their relevance. The selection was based on their significance in understanding the role of digital and AI technologies in smart sustainable cities, providing a comprehensive lens to examine fundamental concepts, existing challenges, and practical applications.

The identified themes include (1) Smart Sustainable Cities, which explores the concept and its relevance to the Global South; (2) Digital and AI Technologies for Driving Smart Sustainable Cities, which examines available technologies available for driving Smart Sustainable Cities; and (3) The Potential of Digital and AI Technologies in Smart Sustainable Cities, which highlights opportunities and benefits. Additionally, (4) Barriers to Adoption and Implementation identifies challenges hindering integration, while (5) Specific Applications of Digital and AI Technologies in the Global South focuses on contextual applications. Furthermore, as mentioned before, case studies were selected and discussed to reinforce the findings from the literature review.

4. Results

4.1. Literature Search

The detailed process of the literature search, review, and quality assessment resulted in the selection of 167 sources, including 143 eligible articles and 24 reports to be included for detailed analyses in the study. The sources comprise 113 (67.66%) journal articles, 12 books (7.19%), 12 book chapters (7.19%), 5 conference proceedings (2.99%), 24 (14.37%) reports and web articles, and 1 (0.60%) thesis. A summary of the reviewed articles is provided in

Table 6. Summary of literature reviewed.

Literature Source	Total (Numbers)	Share (%)	Global South (Number)	Share (%)
Journal	113	67.66	57	39.86
Books	12	7.19
Book chapters	12	7.19
Conference proceedings	5	2.99
Reports/web articles	24	14.37	15	62.5
Thesis	1	0.60
Total	167	100.00	73	43.71

. Out of the total articles and reports reviewed, 57 research articles including book chapters, and 16 reports—accounting for 73 (43.71%)—focus on various aspects of smart and sustainable cities in the Global South. Given the limited research on this topic, the proportion of articles included in the review is considered acceptable. Furthermore, as mentioned previously, four countries from the Global South were analysed as case studies.

Figure 3 illustrates the PRISMA flow chart detailing the stages of identification, screening, eligibility, and inclusion in the study, along with the respective number of articles at each step.

4.2. Research Trends and Focus of Studies on Smart Sustainable Cities

The research trends and focus of studies related to smart, sustainable cities were explored through bibliographic analyses. The co-occurrence of author keywords network analysis (Figure 4) indicates that research is concentrated in six distinct clusters, represented by different colours in the figure. These clusters primarily focus on smart cities and sustainable cities; urban planning, urban policy, and governance; digital technology and artificial intelligence (AI); big data, machine learning, and cloud computing; COVID-19, city logistics, and innovation; and social sustainability, environmental sustainability, and sustainable mobility. Among these, the overarching and central themes are smart cities and sustainable cities, followed by digital technologies and AI. The network also highlights substantial interlinkages between the domains of smart and sustainable cities, digital technologies, and AI. However, as shown in Figure 5, topics such as digital transformation, digital twins, innovations, and application areas like urban development, urban innovation, smart governance, and smart mobility remain peripheral to the main research focus (Figure 5).

The co-authorship by country network (Figure 6) and citation by country network analysis (Figure 7) reveal that most research contributions originate from authors in the Republic of China, the USA, the UK, Spain, Germany, India, Italy, France, and Sweden, often through collaborative efforts (Figure 6). These collaborations are predominantly concentrated within Global North nations and China, with limited engagement involving Global South nations. Sporadic collaborations have been observed with countries such as Malaysia, Morocco, India, South Africa, Oman, and Saudi Arabia. The citation network reflects a similar trend, with significant contributions from the Republic of China and the Global North nations. However, some Global South nations—such as India, Brazil, Saudi Arabia, and Morocco—have made noteworthy contributions, though most Global South nations remain underrepresented in this research domain (Figure 7).

The bibliographic coupling and source network analysis further indicate that prominent journals in this field include Sustainability, Sustainable Cities and Society, Cities, Smart Cities, IEEE Access, Energies, Journal of Cleaner Production, and Frontiers of Sustainability. These journals exhibit connections and shared references, as shown in Figure 8, underscoring their thematic alignment (Figure 8). Despite these insights, the analysis highlights that, while significant research has focused on smart cities and sustainable cities independently, studies integrating these concepts with digital technology and AI remain relatively recent and peripheral. Furthermore, contributions from Global South nations and authors are limited, and the interlinkages among publication sources are similarly constrained.

Thus, this analysis underscores a significant research gap in integrating digital technology, AI, and the concept of smart sustainable cities, particularly in the Global South. Addressing this gap requires the further exploration of the potential applications, barriers, and relevance of digital technologies and AI in developing smart sustainable cities within this context. Consequently, subsequent analyses will focus on these critical aspects to bridge the identified gaps and advance the field.

4.3. Potentials, Applications, and Barriers to Digital Technology and AI Driving Smart Sustainable Cities

4.3.1. Potential of Digital Technologies and AI in Smart Sustainable Cities

The incorporation of digital technologies is pivotal for the creation of smart sustainable cities. For example, ICT plays a critical role in modern cities, enhancing efficiency, connectivity, and citizen engagement across various sectors. It enables cities to collect, analyse, and utilise large amounts of data for improved decision-making and service delivery [104]. It also enables the integration of smart grids for energy management, smart transportation for traffic optimisation, and smart healthcare [13,101]. These technologies assist in improving operational efficiency and enhance sustainability by reducing resource consumption and emissions [18]. Furthermore, ICT is evidenced to foster citizen participation through digital platforms and e-government services, promoting transparency and accountability in governance [105].

The IoTs play a critical role by enabling real-time data collection and monitoring across various urban systems, facilitating efficient resource management and enhancing service delivery [24,47,106]. This capability would allow cities to optimise functions such as energy usage, traffic management, waste management, and water distribution, leading to improved operational efficiency and reduced costs [15,107,108].

Complementing the IoTs, big data analytics offer critical insights into urban patterns and trends, empowering city planners to make data-driven decisions that enhance urban planning and management [16,62,79]. These analytics help identify areas that require improvement and predict future needs, thereby supporting the creation of more liveable and adaptable urban environments [62,109].

Similarly, smart mobility solutions are a vital component of sustainable urban development. Technologies such as electric vehicles (EVs), bike-sharing systems, and smart traffic management systems significantly enhance urban mobility while minimising the environmental impact [15,37,82,107,110].

In the domain of AI, ML and its advanced subset, DL, are revolutionising urban management and services by enabling cities to harness the power of data. ML algorithms empower computers to learn from data and make informed decisions [89,93,111,112]. In cities, ML is pivotal for predictive maintenance, which helps in identifying when infrastructure or equipment requires attention, thus preventing costly failures and enhancing operational efficiency [113]. Additionally, ML is crucial for fraud detection by analysing transaction data to identify anomalous patterns indicative of fraudulent activities, thereby ensuring financial integrity and security [80]. Moreover, ML facilitates customer personalisation, enabling cities to tailor services and recommendations to individual preferences, thus improving citizen satisfaction and engagement [114,115,116].

Deep learning can leverage neural networks with multiple layers to analyse large datasets and uncover intricate patterns [117,118]. For example, DL applications such as image and video recognition are essential for urban security and surveillance, helping to identify objects, faces, and scenes in real time [119]. Furthermore, DL underpins energy optimisation and autonomous vehicle technology, enabling self-driving cars to navigate and make decisions in complex urban environments, thereby enhancing transportation safety and efficiency [36,120].

NLP models enable cities to develop advanced chatbots and virtual assistants that can understand and generate human language, facilitating efficient customer service and support [121]. NLP also supports opinion analysis, allowing cities to gauge public opinion and sentiment from textual data, which is vital for responsive governance and public relations [122].

Computer vision can interpret and analyse visual data, making it indispensable for urban applications such as facial recognition, which enhances security by verifying individuals’ identities [123]. It is also used in object detection, helping to monitor and manage urban infrastructure by locating and identifying various objects within a cityscape [124]. Additionally, in the medical field, computer vision aids in diagnosing diseases through the analysis of medical images like X-rays and MRIs, thereby improving public health services [125].

Reinforcement learning, a dynamic form of ML, is vital for developing AI that can make decisions by learning from interactions with its environment and optimising actions based on rewards and penalties [126]. In urban contexts, game AI utilises intelligent systems for complex problem-solving, robotics applies it to teach robots intricate tasks like assembly line operations, and autonomous driving leverages it to enable vehicles to make real-time decisions in dynamic traffic conditions, thereby enhancing safety and efficiency in transportation systems [127].

Moreover, RPA can help transform urban management by automating repetitive and rule-based tasks traditionally performed by humans [128]. RPA applications include data entry, streamlining administrative tasks and reducing human error, invoice processing that enhances financial operations, and handling routine customer service queries, improving efficiency and service delivery [129].

Thus, integrating digital technologies and AI offers significant potential for applications in urban development and management, promoting smarter, more efficient, and sustainable cities [130]. Therefore, specific areas of application that can drive smart sustainable cities in the Global South are discussed in the following subsections.

4.3.2. Barriers to Digital and AI Technology Implementation

Several technological, and organisational barriers impede the implementation of digital technologies and AI [54]. Organisational barriers encompass constraints in human, financial, and technological resources, as well as challenges related to employee and managerial dynamics, municipal initiatives, cultural factors, and regulatory frameworks. Issues such as mass unemployment, public apprehensions and distrust of AI, the absence of comprehensive legal frameworks, and adverse impacts on sustainability and economic growth also act as barriers. Technological barriers include concerns related to privacy, cybersecurity, the disruptive impact of digital technologies and AI, the limited transparency and accountability, ethical dilemmas, complex decision-making, digital inequality, the intricacies of digital and AI technology use, and challenges with data quality and availability [54].

However, the most significant systemic barriers are identified as infrastructural deficits, the digital divide and inequality, policy and governance challenges, and financial constraints [10,112,117,131,132,133]. These barriers pose substantial challenges to the widespread adoption of digital technologies and AI. The subsequent subsections present the implications of each of these barriers.

Infrastructure Deficits

Cities in the Global South face significant challenges in infrastructure development, which is a fundamental barrier to implementing advanced technologies and smart city initiatives. Many of these cities lack the necessary infrastructure, such as robust broadband networks, efficient transportation systems, and reliable energy grids, to support the deployment of AI and other digital technologies essential for sustainable urban development [10,11,17,38,112,117,134]. The absence of these critical infrastructures limits the capacity of cities to integrate and benefit from AI technologies, thereby hindering the progress toward smart sustainable city models.

Digital Divide and Inequality

A critical challenge for cities in the Global South is that there are significant disparities and inequality in access to essential services such as education and healthcare, often exacerbated by the uneven distribution of technological resources [131,132,135]. The difficulty lies in deploying digital technologies and AI to bridge the gap between different socio-economic groups, ensuring that advancements in smart sustainable city initiatives do not exacerbate the existing inequalities but rather promote social inclusion [131,132]. Disparities in access to technology and digital literacy pose significant challenges to adopting smart sustainable city solutions [49,136].

Policy and Governance

Effective policies and governance frameworks are essential for facilitating the integration of digital technologies and AI and ensuring equitable benefits [135]. Integrating AI and digital technologies into urban planning and development poses a challenge due to the high costs and technical expertise required [137]. This limitation hampers the creation of job opportunities, innovation, and the competitiveness of cities, thus impeding economic growth and development [138]. The challenge is to create a policy and governance environment that can leverage technological advancements to drive inclusive and sustainable development.

Financial Constraints

Cities in the Global South face significant financial constraints that impede the implementation of digital technologies and AI, which are needed in order to succeed in large-scale smart city projects and attain sustainability. Limited access to funding restricts investments in essential infrastructure such as broadband networks, smart grids, and data centres, which are crucial for adopting advanced technologies [112,135]. These cities often struggle to secure external funding due to stringent requirements from financial institutions, and the high costs involved make smart city projects less attractive to private investors [112,135]. Competing demands for public funds prioritise immediate needs like healthcare and education over implementing advanced technology, further limiting financial resources for smart sustainable city initiatives [139]. Additionally, ongoing operational costs for maintaining digital technologies pose a financial burden that many cities find difficult to sustain [140].

4.3.3. Applications of Digital and AI Technologies for Smart Sustainable Cities in the Global South

As discussed previously, digital technology and AI play a vital role in developing and managing smart sustainable cities. In the Global South, where rapid urbanisation and resource constraints present unique challenges, both digital technologies and AI offer innovative solutions for improving sustainability and resilience through direct communication, real-time data analysis, predictive modelling, and decision-making processes essential for managing complex urban systems. For example, AI is used to track driver travel behaviour in Bengaluru, India; Cape Town’s Smart Waste Initiative employs IoT sensors to optimise waste collection; and Rwanda’s AI-powered drone delivery system ensures timely medical supply distribution. The specific domains where these technologies can play critical roles in enabling the creation and management of smart sustainable cities include the following:

Urban Planning and Management

The application of digital technology in urban planning and management is well-established. However, the advent of AI has offered enormous potential in making urban planning and management more efficient and rigorous. For example, AI-driven tools can optimise urban planning by analysing data and creating scenarios for population growth, traffic patterns, energy consumption, environmental impacts, water management, waste management, etc. These tools can enable city planners to develop strategies that balance development with sustainability, ensuring efficient land and resource use and infrastructure development [16,26]. For example, innovative technologies like the Open Building Insights (OBI) platform and the Modeling Urban Growth (MUG) AI model represent a significant step forward in understanding how cities in Africa and India are likely to evolve. By combining satellite imagery, demographic data, and advanced machine learning, these tools offer policymakers critical insights into future infrastructure needs and urban growth patterns [141].

Transportation and Mobility

AI improves transportation systems by optimising traffic flow, reducing congestion, and enhancing public transportation. Smart mobility solutions, such as AI-powered traffic management systems and predictive analytics, can reduce travel times, enable route choice, lower emissions, and improve urban transportation efficiency [37,81,82,83]. These benefits are evidenced by examples from some Indian cities and Kigali, Rwanda. For instance, an AI camera and facial recognition system in Bangalore could monitor driver behaviour, such as fatigue from overwork and speeding. Similarly, the condition of buses is monitored through driver fatigue detection, surveillance cameras on vehicles, improvements in bus efficiency, and sensors that manage pedestrian signal crossings. In Chennai, automatic number plate recognition cameras powered by Optical Character Recognition (OCR) read traffic violations and automatically generate demand invoices for the payment of a fine sent to the violator [80]. In Kigali, IoT is used for Car Parking Spaces Management for Traffic Congestion Mitigation [107].

Energy Management

IoT and AI enhance energy management in smart sustainable cities by optimising energy production, distribution, and consumption. AI systems integrate renewable energy sources, monitor energy usage, and predict future energy demands, contributing to more sustainable energy systems [84,85,86,142]. For instance, the Puducherry Smart Grid Pilot Project in India has proven valuable in enabling the real-time monitoring of energy consumption patterns and related alarms through the Advanced Metering Infrastructure (AM) system. It also features environmentally friendly electric vehicles with charging facilities powered by solar photovoltaic (PV). Additionally, it demonstrates a Smart Home Energy Management System to encourage consumer participation, along with functionalities like smart security and microgrid controllers [143].

Waste Management

AI transforms waste management by facilitating real-time monitoring and predictive analytics to enhance the efficiency of waste collection, recycling, and disposal. For instance, Cape Town is enhancing waste management through smart systems that use sensors and data analytics to optimise collection routes and improve efficiency. The real-time monitoring of bin levels ensures timely collection, reducing overflow and littering. Additionally, the city is exploring the use of drones for waste management [144]. Smart waste management systems with the use of IoTs reduce costs, minimise environmental impacts, and improve resource recovery rates [5,30,52,87,88,89,145].

Environmental Monitoring

Digital technology, including AI, facilitates environmental monitoring by analysing data from sensors, satellites, and social media to track pollution levels, monitor biodiversity, and predict natural disasters. For example, a real-time air pollution monitoring and forecasting system, supported by Internet of Things (IoT) sensors and AI, can detect a wide range of air pollutants, including ammonia (NH₃), carbon monoxide (CO), nitrogen dioxide (NO₂), methane (CH₄), carbon dioxide (CO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter (PM2.5 and PM10). It provides real-time data on pollutant concentration levels [146]. These insights help cities implement proactive measures to protect the environment and enhance climate resilience [90,91,92,93,94].

Governance and Participation

Digital technology such as ICT, IoTs, and AI enhance governance in smart sustainable cities by enabling more transparent, efficient, and inclusive decision-making processes. For example, ICT is being used for communication and information transfer for citizen participation in India, specifically in smart cities such as Cochin, Bhubaneswar, Kolkata, etc., although the challenges of the digital reduce the impact [10,24]. AI-driven platforms facilitate citizen engagement, improve service delivery, and support data-driven policy development, ensuring responsive urban governance [24,81,82,83,84,85,86].

5. Case Study Analyses

Several counties in the Global South have successfully either implemented or are in the process of implementing digital technologies and AI to promote sustainability, make some of their cities smart and sustainable, and, consequently, improve the quality of life. In the absence of examples of specific cities in the Global South, the following country-level case studies illustrate the potential of digital technologies and AI to drive smart sustainable city development.

5.1. India

India has launched several initiatives that incorporate digital technology and AI to address urban challenges such as resource management, identity verification for accessing public services, financial transactions, traffic management, pollution, etc. For example, India’s digital landscape has been revolutionised by the Aadhaar system, a biometric-based digital identity initiative that, despite some challenges, streamlines access to services and secure identity verification [147]. Alongside Aadhaar, the Unified Payments Interface (UPI) has advanced financial transactions by enabling efficient digital transactions, especially in urban areas, enhancing governance and reducing fraud [148]. The shift to digital payments has simplified financial interactions, reduced corruption, and provided valuable data for informed policymaking, demonstrating the transformative impact of a digital infrastructure on building a smart sustainable society. Furthermore, cities are exploring avenues to implement service delivery, such as traffic management, waste management, construction, etc., using AI [34,149]. For example, as mentioned previously (Section 4.3.3) in Bangalore, AI-powered cameras with facial recognition monitor driver behaviour, including fatigue from overwork and speeding. At the same time, sensors and surveillance systems enhance bus efficiency and pedestrian safety. In Chennai, OCR-based automatic number plate recognition cameras detect traffic violations and issue fines directly to offenders [82]. However, integrating AI into urban development is hindered by a significant digital divide, with disparities in Internet access and digital literacy between different sections of the people [150,151]. This gap limits the reach of smart city solutions. Further, concerns over data privacy and security pose challenges, as the extensive deployment of sensors and surveillance systems in AI-driven cities leads to the collection of vast amounts of personal data, raising issues related to data protection and public trust [152].

5.2. Kenya

AI is being implemented across various sectors in Kenya, including agriculture, healthcare, education, fintech, and transportation. The country is actively positioning itself to leverage the benefits of Fourth Industrial Revolution (4IR) technologies such as AI. Furthermore, as East Africa’s leading tech hub, Nairobi supports innovation through tech incubators and co-working spaces in Nairobi, fostering economic growth and solidifying its status as a centre for digital innovation [153]. In 2018, the government established the Blockchain and AI Taskforce to guide the strategic use of AI [154]. Moreover, the energy distribution system is transformed by adopting ICT and AI-enabled smart grid technologies that enhance efficiency and reliability. AI-powered smart meters also provide real-time data on energy consumption, helping to reduce wastage and support sustainable practices [49,155]. However, the absence of comprehensive and structured AI-specific policies and the regulatory and policy gaps pose challenges related to data privacy, ethical AI use, and legal compliance, thus hindering the implementation [156,157].

5.3. Rwanda

Rwanda has made significant progress in developing a smart sustainable city by incorporating digital and AI technologies into its urban planning and management processes. The country has introduced a digital land registry, modernising and securing land ownership records [76]. Supported by a robust digital infrastructure, this initiative revolutionises land transactions, reduces fraud, and improves record accuracy, leading to greater transparency and efficiency in the real estate sector. Additionally, the adoption of digital technology in land management has streamlined processes and positively influenced governance by minimising corruption, boosting accountability, and supporting informed decision-making in urban planning [158]. Similarly, the “Tap&Go” smart card system for public transport has streamlined fare payments and improved commuter efficiency, helping address urban mobility and reducing traffic congestion [159]. Furthermore, IoT is utilised for managing car parking spaces to mitigate traffic congestion [107]. However, Rwanda faces challenges in digital technology and AI adoption due to a shortage of skilled professionals in AI and digital technologies, which slows the deployment of smart city initiatives. Moreover, the lack of structured data ecosystems impedes the effective deployment of AI solutions [160,161,162].

5.4. Brazil

Brazil leverages ICT and AI in urban planning to tackle challenges from rapid urbanisation. The São Paulo Research Foundation, a leading scientific institution in Brazil, has partnered with IBM to establish the first Latin American branch of IBM’s AI Horizons Network [163]. In Brazil, AI has proliferated in the private sector—particularly in areas like e-commerce, finance, and technology-driven companies—and in the public sector. The push for digitalisation to enhance service quality has spurred investment in these technologies. For instance, the Brazilian federal government has launched the Brazilian Strategy for Artificial Intelligence (EBIA) in 2021, which aims to guide state actions to promote research, innovation, and the development of AI solutions [164]. However, Brazil faces several challenges in leveraging AI and digital technologies for smart sustainable cities. Infrastructure gaps, including uneven broadband access, limit AI deployment [165,166]. Moreover, regulatory fragmentation and bureaucratic inefficiencies hinder the effective implementation of smart city initiatives [166]. Socio-economic inequality exacerbates digital exclusion, preventing the widespread access to AI-driven urban services [167]. Cybersecurity and data privacy risks remain concerns due to the weak enforcement of the General Data Protection Law [166].

6. Discussion, Policy Implications, and Recommendations

6.1. Discussion

The evolution of sustainable development discourse has expanded from an initial focus on environmental issues to a more comprehensive consideration of social and economic dimensions [1]. This broader perspective has been instrumental in shaping the concept of sustainable cities, which emerged as a response to pressing challenges such as environmental degradation and the need for urban resilience [2]. Recognising these complexities, the United Nations included the development of sustainable cities and communities in its SDGs (SDG 11), underscoring the need for a multi-dimensional approach that encompasses social, economic, and environmental sustainability [4].

Sustainable cities are envisioned as urban areas that integrate economic opportunities, affordable housing, and comprehensive infrastructure, emphasising public transportation and green spaces, all guided by participatory planning [3,5,7,8]. This model aims to balance current and future needs, fostering inclusive and resilient urban environments. While cities worldwide, including those in the Global South, have adopted this model to varying extents, the parallel emergence of smart cities presents a distinct, technologically driven approach to urban development [10,11]. Smart cities, although lacking a single definitive concept, generally emphasise the integration of digital technologies to enhance urban functions, aiming to create more efficient, liveable, and sustainable environments [9,13,14,82]. These cities utilise advanced digital technology to improve aspects such as governance, mobility, and infrastructure, often focusing on economic efficiency and environmental sustainability [25,26]. However, there is criticism that smart cities sometimes prioritise technological advancements over addressing deeper social and environmental concerns [3,28,32,33].

To address the challenges and leverage the advantages of smart cities while ensuring sustainability, the concept of smart sustainable cities has recently emerged. This concept combines the efficiency of smart technologies with the holistic goals of sustainable development [14,19,24,30,31]. Smart sustainable cities are characterised by efficient resource management, sustainable infrastructure, and enhanced connectivity, which are essential for balancing economic growth with environmental conservation and social inclusion [18,24]. This evolution reflects a significant shift towards using advanced digital tools to meet sustainability targets while improving the quality of life in cities. The adoption of digital technology and AI, such as ICT, IoT, big data, and AI-driven tools, plays a critical role in optimising urban functions, enhancing decision-making, and improving the overall quality of life [16,101,104].

The successful adoption of digital technology and AI in smart sustainable cities in the Global South requires addressing key technological, organisational, and environmental factors in alignment with the Technology–Organisation–Environment (TOE) framework. Technologically, digital innovations offer significant advantages in improving efficiency, sustainability, and urban management [64]. However, infrastructure deficits, the digital divide, and integration complexities hinder their widespread adoption [39,41,112,131,133,136,137,139,140]. Ensuring compatibility, affordability, and scalability is essential. Organisationally, governance structures, financial resources, and institutional capacity must be strengthened [66]. Weak governance, financial constraints, and a lack of institutional readiness pose significant challenges; yet, successful implementations in India, Rwanda, Kenya, and Brazil highlight the importance of strong policy frameworks, and inter-agency collaboration. Investment in institutional capacity building, cross-sector partnerships, and innovative financing mechanisms is necessary. Environmentally, external factors such as regulatory landscapes, market conditions, and infrastructure availability influence adoption [68].

Despite the challenges, cities in the Global South, such as those in India, Rwanda, Kenya, and Brazil, are making strides toward smart sustainable city development by leveraging digital technology and AI to address urban issues and enhance people’s quality of life [24,50]. In the Global South, these technologies offer transformative solutions for urban planning, transportation, energy management, waste management, environmental monitoring, and governance, helping cities address rapid urbanisation and resource constraints [26]. Successful implementations in cities of the above-mentioned countries demonstrate the potential of these technologies for enhancing urban sustainability, making the cities smart and sustainable [24,26].

Thus, while the concept of smart sustainable cities holds promise for the Global South, realising this vision requires overcoming substantial hurdles and adapting technologies and AI to local contexts. Moreover, a framework for the adoption and implementation of digital technology and AI for smart sustainable cities is needed. In this context, a conceptual framework is presented in Figure 9.

The framework is built on three pillars based on the three key factors (Technological, Organisational, and Environmental) of the TOE framework: the adoption of digital technology and AI, the resolution of critical barriers, and the identification of specific application areas in Global South cities. Digital technology and AI play a pivotal role in addressing urban challenges and enabling efficient solutions for essential city functions. As emerged from the study, the key barriers to the adoption of digital technology and AI in Global South cities are infrastructure deficits, the digital divide, policy and governance challenges, and financial constraints. Overcoming these barriers is crucial for enhancing the implementation and effectiveness of digital solutions within the cities of the Global South.

Furthermore, important application areas in Global South cities that can benefit from digital technology and AI include urban planning and management, transportation and mobility, energy management, waste management, environmental monitoring, and governance with stakeholder participation. The study indicates that some of these areas are already being addressed through digital technology and AI in the case study cities.

Addressing these barriers and integrating digital technology and AI into critical urban functions can enhance urban management, improve service delivery, boost competitiveness, and elevate the quality of life, ultimately transforming cities into smart, sustainable cities in the Global South (Figure 9). Strengthening efficiency in these key domains through digital interventions will likely accelerate this transformation. However, the specific barriers and application areas may vary depending on each city’s unique context and priorities. Therefore, policies and strategies for adopting digital technology and AI must be tailored to the local needs and challenges of each city to ensure smart sustainable city development.

6.2. Policy Implications and Recommendations

Policies should be developed and implemented to leverage the potential of digital technology and AI while addressing challenges in the Global South to create smart sustainable cities. Critical policy implications and recommendations include the following:

• The identification and prioritisation of city functions and application areas: Critical city functions that require enhanced efficiency, along with key areas for digital technology and AI applications, should be identified and prioritised based on the city’s specific needs. Municipalities should conduct needs assessments to identify critical city functions, such as transportation, healthcare, waste management, and energy, where digital technology and AI can yield the most significant benefits. Given financial constraints, these assessments should prioritise areas where technology can provide a significant return on investment in terms of efficiency and impact. A collaborative, evidence-based approach involving local experts and stakeholders is essential for aligning digital solutions with the city’s long-term goals while being mindful of budget limitations.
• Enhancing collaboration and partnerships: Collaboration and partnerships between governments, the private sector, and civil society are essential for mobilising resources, including funding for infrastructure and expertise in smart, sustainable city development. Given the resource constraints, collaboration between governments, the private sector, and civil society is vital for mobilising funding and expertise for smart, sustainable city development. Governments should focus on creating public–private partnerships (PPPs) that incentivise private-sector investment in infrastructure. Multi-stakeholder platforms, including civil society, academia, and international donors, should be established to share knowledge, co-finance initiatives, and build capacity. Digital platforms should be utilised to enhance transparency and accountability in partnerships, ensuring the efficient use of resources within financial limitations.
• Strengthening institutional capacities: Building institutional capacities to manage and regulate digital technologies and AI is essential for their effective adoption and sustainable implementation. Governments should strategically invest in building institutional capacities by prioritising training for key and relevant personnel, regulators, and city planners in digital technologies, data management, and AI. Given resource limitations, this can be achieved through partnerships with universities, training programs, and online courses, ensuring continuous skill development without significant financial burden. Establishing dedicated units within local governments focused on digital transformation can streamline policy development and technology adoption while ensuring that regulatory frameworks evolve in line with technological advancements.
• Promoting inclusive development and people’s participation: Policies should prioritise inclusivity in smart, sustainable city initiatives, ensuring they benefit marginalised communities. Digital technology and AI-driven participation are key to bridging the digital divide and promoting inclusivity. Policies should focus on inclusivity in smart, sustainable city initiatives, ensuring they benefit marginalised communities despite financial constraints. Digital technology and AI-driven participation should be prioritised to bridge the digital divide. Given financial constraints, policies can focus on affordable digital literacy programs, utilising existing community centres or local organisations to provide training. Cities can design cost-effective digital platforms that facilitate public participation, leveraging open-source tools and local expertise to engage communities in decision-making. AI-powered community-based monitoring tools can be developed incrementally, ensuring their scalability as resources allow.
• Encouraging innovation and research: Investing in research and innovation is crucial for developing context-specific solutions to the unique challenges faced by cities in the Global South. The focus should be on increasing the targeted funding for research and development that addresses the specific challenges of cities in the Global South. This can be achieved by prioritising projects that deliver high-impact, context-specific solutions within available budgets. Collaborations with universities, research institutes, and tech companies should be established to leverage existing resources and foster innovation. Public-sector innovation hubs can incubate start-ups with low operational costs, providing essential resources like mentorship and access to seed funding to help scale local solutions.

7. Conclusions and Future Research

The development of smart sustainable cities in the Global South is a crucial approach for tackling the complex challenges of rapid urbanisation and promoting sustainable development. Integrating advanced digital technologies and AI into urban planning and management offers the transformative potential to create efficient, resilient, and inclusive cities. However, realising this vision requires overcoming significant barriers, including infrastructure deficits, financial limitations, and governance challenges. The successful implementation of smart sustainable city models hinges on a comprehensive approach that prioritises innovation, inclusivity, and adaptability to local contexts. This approach should prioritise not only technological advancements but also the social and economic aspects, ensuring that the benefits of urban development are shared equitably among all residents. Moreover, fostering smart sustainable cities in the Global South involves a multi-dimensional effort that balances current needs with future aspirations, ultimately contributing to global sustainable development goals.

This paper contributes in terms of identifying the types of digital technologies and AI suitable for smart sustainable cities and examining how they can address urban challenges to promote smart, sustainable development in the Global South. It also explores the barriers to implementing these technologies and the related policy implications for developing smart sustainable cities in this region. Furthermore, a conceptual framework is presented that is likely to offer a way forward for the city development authorities and municipalities in their endeavour to transform their cities into smart, sustainable cities. However, it should be tailored to each city’s specific contexts, needs, and demands.

However, this paper has limitations. It relies on a literature review and a limited case study analysis. The study’s limitations include the reliance on only three databases—Scopus, Web of Science, and Google Scholar—excluding many other potentially relevant databases. Additionally, no automation tools were employed for removing duplicates or merging database records. Furthermore, most of the reviewed articles were qualitative studies, with only a limited number of empirical studies. This reflects the nature of the discipline, the relative newness of the study domain, and the general scarcity of empirical research in this field. Consequently, the findings may not be conclusive and, as the field evolves, significant empirical studies may either reinforce or contest the conclusions drawn.

Additionally, it does not propose a unified vision for urban planning in the Global South but instead offers a conceptual framework for adopting digital technology and AI in the development of smart sustainable cities.

Future research should include context-specific empirical analyses to refine and adapt the conceptual framework to the unique socio-economic, cultural, and environmental contexts of individual cities within the Global South. Additionally, studies on policy and governance mechanisms for effectively integrating digital technology and AI are essential. Moreover, future research incorporating a broader database coverage and more empirical studies would strengthen the validity and generalizability of the findings.