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Review

Artificial Intelligence for Sustainable Industrial Design: A Systematic Literature Review Based on a Technology–System–Institution Framework

by
Xinyu Li
1,
Yingyan Zhang
2,*,
Conghu Liu
3,
Junyi Zhao
4 and
Kai Li
5
1
School of Fine Art and Design, Suzhou University, Suzhou 234000, China
2
School of Management Engineering, Anhui Institute of Information Technology, Wuhu 241000, China
3
School of Mechanical and Electronic Engineering, Suzhou University, Suzhou 234000, China
4
Faculty of Innovation and Design, City University of Macau, Macau 999078, China
5
School of Business, Suzhou University, Suzhou 234000, China
*
Author to whom correspondence should be addressed.
Processes 2026, 14(5), 779; https://doi.org/10.3390/pr14050779
Submission received: 2 December 2025 / Revised: 23 January 2026 / Accepted: 28 January 2026 / Published: 27 February 2026
(This article belongs to the Special Issue Multi Dimensional Value Driven Sustainable Manufacturing Processes and Innovation)
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Abstract

The rapid development of artificial intelligence (AI) is driving the transformation of industrial design towards sustainability. However, a systematic integration framework that can effectively clarify how AI promotes sustainable product design across multiple dimensions remains lacking. This systematic review comprises a detailed analysis of 113 core articles from the Scopus and Web of Science databases (covering 2015–2025), following PRISMA guidelines, examining publication trends, key journals, and citation impacts. From the perspectives of technology, systems, and institutions, this study systematically analyzes AI technologies and their possible application in promoting sustainable industrial design. Based on these findings, the challenges in applying AI in industrial design sustainability are discussed, such as technological controllability, system integration barriers, and policy lags. Key directions for future research are also identified. This review constructs a multi-dimensional framework to systematically explain the applications and mechanisms of AI in promoting sustainable industrial design. It also offers clear theoretical foundations and practical guidance for researchers, practitioners, and policymakers, facilitating the advancement of industrial design in a more sustainable and systematic direction.

1. Introduction

1.1. Background

AI is a technology that uses computer programming systems to mimic human cognitive processes. It is used to process large volumes of labeled training data, analyze reasoning patterns and correlations within the data, and apply these patterns to predict future outcomes. Recent advances in deep learning and AI technologies, along with the application of generative AI tools, such as Midjourney, Stable Diffusion, and ChatGPT, using large language models, are drivers of the sustainable transformation of industrial design [1,2]. AI has shown immense potential in promoting sustainable development and is gradually becoming a focal point of interdisciplinary research.
At the technical application level, AI has been deeply integrated into the entire industrial design process, including user preferences [3,4], design proposals, concept generation, and physical prototype design. Specifically, deep learning, artificial neural network algorithms, and natural language processing techniques run [5] through all stages of conceptual design, planning, implementation, and quality control, constituting the core framework for promoting intelligent manufacturing in the context of Industry 4.0. However, there is little research on how AI facilitates systematic collaboration across all stages of design, manufacturing, and product lifecycle, especially at the process industry level, necessitating further investigation.
The integration of AI into industrial design is driving a shift in product design from the traditional “production–consumption–disposal” model to one that focuses more on resource optimization, waste reuse, and circular economy models [6]. AI has been applied in industrial equipment lifecycle management [7], intelligent decision-making [8], smart manufacturing [9], process automation [5,10], and engineering design processes. Jamwal et al. noted that AI and machine learning, as key Industry 4.0 technologies, are being used across industries to analyze product lifecycle data to enhance sustainability and efficiency in manufacturing [11]. AI is a key technological force driving industrial sustainable development through critical changes, such as energy efficiency improvements, resource recycling, and pollution control in the industrial field [12]. Carpanzano and Knüttel proposed that AI technology can be applied to industrial control systems, ranging from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, and from factory adaptive automation systems to production monitoring systems [13]. Therefore, AI-driven big data analysis systematically improves sustainable manufacturing.
Despite the broad technological prospects of AI, its implementation still relies on coordinated evolution at the institutional level. This process is associated with two challenges. First, the in-depth application of AI is accompanied by significant ethical and governance issues. Lee proposed that AI can enhance the social sustainability of product design, emphasizing the importance of considering the diversity of social sustainability to promote more comprehensive sustainable development [14]. Li et al. systematically discussed the ethical issues associated with the application of AI technology in the healthcare sector [15]. Bansal highlighted the transformative potential of AI in supporting sustainable development, as well as the urgent need for ethical frameworks, regulatory oversight, and interdisciplinary collaborations to ensure the sustainability of AI [16]. Second, building the relevant framework requires multi-party collaboration. This literature review finds that the key driving force promoting the sustainable implementation of industrial design comes from government laws and policies. Sharma et al. proposed that all relevant stakeholders, including governments, policymakers, industry, and academia, must fully consider the safety, transparency, traceability, explainability, effectiveness, and verifiability of AI during its development and application to address potential risks and challenges [17].

1.2. Research Gaps and Research Questions

With the development of AI and the continuous advancement of research on sustainable industrial design, there is an increasingly urgent need for a systematic and comprehensive review to synthesize the existing findings. Although some scholars have conducted reviews in this field, their studies suffer from three limitations. First, they focus on the application of AI at the technical level of industrial design, not addressing obvious system-level limitations that cannot be solved easily to meet the comprehensive needs of this complex problem. For example, although some studies have explored the application of AI in product design, manufacturing, and recycling [18], they often focus on a single stage, and there is a lack of systematic integration of core design, production, and recycling systems. Second, the exploration of the institutional dimension is limited [19]. Existing studies often neglect the interrelationship between technological pathways and institutional factors such as policy, ethics, and governance, leading to a disconnection between technological applications and social regulations. Third, the analyses have not been performed at sufficient depth. Some existing reviews are overly reliant on bibliometric methods, offering analyses that remain at the descriptive statistics level and lack an in-depth exploration of key research issues, theoretical frameworks, and practical implications. More importantly, despite the rapid publication of studies in this field, these studies fail to effectively link AI technology applications with current strategic pathways for achieving sustainable development goals, making it difficult for them to provide a solid theoretical foundation for the sustainable development of industrial design. To address this research gap, this study employs interdisciplinary quantitative integration and aims to answer the following five core research questions (RQs):
RQ1: What are the development trends, current status, and key research areas regarding AI in the field of sustainable industrial design?
RQ2: At the technical level, which key technological paths and AI application scenarios drive industrial design sustainability?
RQ3: At the system level, how does AI facilitate the integration and optimization of core systems such as design, production, and recycling to construct a holistic, sustainable paradigm?
RQ4: At the institutional level, what are the key policy guidance needs, ethical challenges, and governance framework issues challenging the application of AI?
RQ5: What are the challenges and future directions for AI in sustainable industrial design from an integrated technology, systems, and institutional perspective?
The theoretical value of this study lies not only in its multi-dimensional perspective on AI, industrial design, and sustainable development but also in the construction of an integrated framework at the “technology–system–institution” level. This study extends Gerber’s multi-objective optimization framework to the field of institutional design [20]. Its practical significance is linked to deconstructing the core of technological innovation, revealing system coordination mechanisms, and exploring in depth paths of institutional innovation and improvement. This study provides a practical means of achieving more efficient and sustainable industrial design, presenting theoretical support and practical guidance for both academia and industry in advancing the digital transformation and sustainable development of industrial design.

2. Materials and Methods

This review employed a systematic literature review (SLR) method and adhered to the guidelines outlined in the most recent 2020 revision of the PRISMA methodology (Table S1). The PRISMA framework ensures the transparency and reproducibility of systematic reviews, safeguarding the rigor and consistency of the research. Furthermore, this review referenced the literature search strategy proposed by Peres et al. [21], who thoroughly describe the predefined steps for conducting a systematic literature review, including the identification, screening, eligibility assessment, and inclusion phases.

2.1. Search Strategy

A systematic literature review (SLR) was conducted following established guidelines. The Scopus and Web of Science databases were the primary sources for literature retrieval. These two databases were chosen because they comprehensively cover peer-reviewed articles and conference papers and are widely used in systematic reviews in the fields of engineering and science. The literature search covered the period from 2015 to 2025. Since 2015, core technologies such as deep learning and generative models have gradually matured and begun to be systematically applied in industrial design, manufacturing optimization, and sustainable product development. Selecting this time interval is helpful to comprehensively grasp the key technical progress and research trends of AI-driven sustainable industrial design.
A list of search keywords was established to identify potentially relevant papers within the search scope via an iterative process. Search terms related to AI were compiled, including “AI”, “artificial intelligence,” “machine learning,” and “deep learning”.
Identification: During this process, filters consisting of keywords and Boolean operators were used to filter records. Searches were conducted in the Scopus Core Collection databases and Web of Science, with a date range of 2015 to 2025, targeting titles, abstracts, and keywords. For Scopus, the search was performed using titles, abstracts, and keywords (TITLE-ABS-KEY). The search criteria were: (“AI-Driven” OR “digital technology” OR “digital” OR “generative artificial intelligence” OR “AI” OR “artificial intelligence” OR “machine learning” OR “deep learning”) AND (“industrial design” OR “industry design” OR “product design” OR “industrial system*”) AND (“sustainable” OR “sustainable development” OR “circular economy” OR “eco-design” OR “green manufacturing”). In Web of Science, we searched by subject (TS), including the title, abstract, author keywords, and Keywords Plus. In this study, multiple rounds of testing and iterative optimization were performed on the search formula to eliminate irrelevant records and improve the accuracy of the results as much as possible.
Screening: After the search, the team de-duplicated the records from the two databases and identified 522 independent articles. To avoid duplication bias, the search results from Scopus and Web of Science were exported in RIS format and imported into Zotero, where potential duplicates were flagged based on title, authors, publication year, journal, and DOI. Two reviewers manually checked these flagged records and resolved discrepancies through discussion (with a third reviewer arbitrating when necessary). The de-duplicated records were then screened for eligibility by two reviewers, excluding non-English or inaccessible sources and studies unrelated to industrial design. Papers addressing AI or sustainable development were retained, while those focusing solely on manufacturing methods or unrelated optimization techniques were excluded. Cross-disciplinary studies (user experience, visual, fashion, or wearable design) were included if they addressed sustainability and digital technologies.
Qualification review: This stage primarily involved reviewing titles, abstracts, and keywords to further assess the eligibility of the studies. After further verification, 476 articles were included for eligibility assessment. Out of these, 363 articles were excluded based on the established criteria.
Inclusion: This step involved reading the full texts and reviewing the inclusion criteria (Table 1). Through reading the full text, the relevance of each article to the topic of AI was further confirmed. Ultimately, 113 articles that were aligned with the research topic were retained for further analysis (Figure 1).

2.2. Data Analysis

In total, 113 articles aligned with the scope of this study were analyzed. Bibliometric information was collected, providing descriptive statistics on publication time, source journal, citations, subject area, and other dimensions, revealing trends and hotspots in research on AI in sustainable industrial design.
On this basis, this review further involved inductive coding to analyze the content of the articles. By reading the titles, abstracts, keywords, and full texts, the research topics were open-coded and clustered according to topic similarity. The research topics were classified into three dimensions: technical, systematic, and institutional. The technical dimension focuses on the technical path of AI in industrial design, the system dimension focuses on the system coordination mechanism, and the institutional dimension covers issues such as policy orientation, ethical norms, and governance frameworks.
To ensure the reliability of the coding results, coding was performed independently by two researchers who reached agreement through comparison and discussion. When disagreement occurred, a third researcher was introduced to adjudicate, and a third researcher was selected for the review to reduce subjective bias and improve coding consistency.

3. Descriptive Analysis of Included Studies

RQ1: What are the development trends, current status, and key research areas of AI in the field of sustainable industrial design?

3.1. Research Trends

Figure 2 illustrates the trends in AI publications in the field of industrial design sustainability research. Between 2015 and 2020, there were relatively few papers focusing on the intersection of AI and industrial design sustainability. This indicates that the field was still in an early, exploratory stage at that time. During this period, academic research in industrial design had just started to shift towards the application of AI technologies, and related studies had not yet gained significant attention. Since 2021, the number of publications has increased rapidly, especially in 2025 (43 papers). This significant increase reflects the growing recognition of the potential of AI to enable sustainable development in industrial design and the substantial academic impact it has generated. Given the current strong momentum of research and development, it is predicted that the intersection of AI, industrial design, and sustainability will remain a core research direction in the future of industrial design (Figure 2).
Additionally, through the analysis of 113 articles, this study identifies three main research directions in the field of industrial design: 59 articles (52%) focus on technology research, 37 articles (33%) focus on system research, and 6 articles (5%) focus on institutional research. Furthermore, some studies put forward suggestions or requirements for relevant policies based on the exploration of technologies or systems, with a total of 11 (10%) of such policy-oriented cross-cutting studies (Figure 3).

3.2. Source Journals

Table 2 shows the distribution of papers published on AI in the field of industrial design sustainability in the top ten peer-reviewed journals, ranked by the number of papers. The journal Advanced Engineering Informatics published the most papers (8), followed by Sustainability (6) and the Journal of Cleaner Production (5). The table also indicates that Elsevier and Springer are the leading publishers in this research field. Notably, many of the journals have high impact factors or CiteScores, reflecting the recognition of research in this field by high-level academic platforms and further validating the academic value of the related research outcomes (Table 2).

3.3. Most Cited Publications

Table 3 lists the top ten representative papers in this field based on citation count. The top three most cited studies are Wu et al. (TC: 354) [22], a review on the material mechanics of additive manufacturing; Liu et al. (TC: 316) [23], which built a “CAB2IN” sustainable supply chain management framework for the circular economy through digital technology; and Gebhardt et al. (TC: 224) [24], a review on circular supply chain collaboration. Early studies in this field mainly focused on additive manufacturing, the circular economy, and related areas. With the maturation of AI technology, recent research has focused on integrating AI with the circular economy, circular supply chains, remanufacturing, and other related fields. This demonstrates a clear trend of moving from technological exploration to systematic, cross-disciplinary applications (Table 3).

3.4. Top Subject Areas

Figure 4 shows the distribution of 113 articles across various subject areas. The analysis revealed that “Engineering” is the dominant discipline in this field. The next largest disciplines are “Computer Sciences” and “Social Sciences,” reflecting the interdisciplinary nature of the research topic. In the computer science field, research focuses on the application of AI technologies, such as machine learning algorithms and model optimization. In the social sciences field, research focuses on the integration of product design (Figure 4).

3.5. Current Research Concerns

We analyzed keyword co-occurrence using VOSviewer (version 1.6.20). The keywords indicate the research topics involved, and the co-occurrence map reflects the semantic similarity between these topics. The keywords were grouped into different clusters based on their co-occurrence, with the proximity between clusters reflecting the strength of relationships between the themes (Figure 5).
Artificial Intelligence (red area): The keywords are primarily concentrated on industry 4.0, digital technologies, and big data, highlighting the extensive application of AI in industrial design.
Machine Learning (green area): The keywords focus on learning algorithms and algorithm optimization, highlighting the role of machine learning in advancing product design and optimizing processes, thus reflecting the important role of AI in design.
Product Design (yellow area): The keywords focus on the circular economy, sustainability, and management, indicating a shift toward systematic and ecological strategies in the field, with an emphasis on sustainability and resource management in design.
Sustainable Development (blue area): Topics such as “environmental impact” and “lifecycle assessment” reflect the growing research on sustainability issues in industrial design, especially in promoting green manufacturing and the circular economy.
The co-occurrence diagram plotted by year (Figure 6) shows that the year of publication for the clusters of Sustainable Development, Product Design, and Artificial Intelligence is generally similar, with the most common publication year being 2022. In contrast, the most common publication year for Machine Learning is 2023, indicating rapid growth in the application of this technology in sustainable industrial design. More importantly, the results highlight that current studies strongly emphasize the technological dimension, while system integration and institutional development remain relatively underexplored. This “technology-heavy, system-light, institution-weak” research pattern hinders the full realization of AI’s value in advancing sustainable industrial design.

4. Fundamental Issues

Based on the third part of the analysis, this study constructs an analytical framework based on the three dimensions of technology, system, and institution to divide the core theme areas of AI driving the sustainable development of industrial design (Figure 7).
RQ2: At the technology level, which key technological paths and application scenarios of AI drive industrial design sustainability?

4.1. Technology Level: The Fusion of AI and Industrial Design

4.1.1. Industrial Design Optimization

AI technology has been widely applied to key stages of industrial design, including creative generation, scheme design, and evaluation feedback. In the concept design phase, algorithms such as Midjourney [32] and Stable Diffusion (SD) [33,34] can rapidly generate a large number of product concept sketches. During the scheme design phase, text generation models (e.g., ChatGPT), image generation tools (e.g., Midjourney) and color harmony theory, with quantitative evaluations of shape and color combinations, are used to determine the optimal product design [35,36,37]. The principles of aesthetic measurement, combined with genetic algorithms (GAs), have been applied to optimize product morphology [38]. Machine learning techniques, including radial basis function artificial neural networks (RBF ANNs), GAs, probabilistic neural networks (PNNs), and support vector regression (SVR), are employed to quickly and intelligently provide designers with high-quality, personalized design solutions while supporting sustainable development [39]. However, despite the potential demonstrated by AI in specific design stages, its systematic application throughout the entire design process remains insufficient. Research has indicated that technologies such as artificial neural networks have been used in the ecological design of industrial products, but existing solutions have yet to fully cover the entire process from planning and design to the product’s lifecycle [40].

4.1.2. Product Preference Prediction

AI can rapidly and accurately understand users’ emotional and aesthetic needs for industrial products and translate these into design elements, thereby playing a key role in aligning product design with sustainability goals [32,41]. Currently, industrial product preference prediction primarily follows two technical paths.
The first approach involves emotional design based on deep generative models (e.g., GAN, diffusion models) [33]. This approach translates users’ emotional needs into design elements to generate personalized products. For instance, Wang et al. combined the advantage map entropy weight method with stable diffusion models to uncover users’ emotional needs [42]. Liu et al. integrated game theory (GT) and AIGC to help design departments develop creative products that meet consumers’ emotional needs [43].
The second approach focuses on data-driven design parameter optimization. Relevant studies have employed algorithms such as convolutional neural networks (CNNs), random forests (RFs), neural networks, and gradient boosting decision trees (GBDTs) to analyze user behavior, ergonomics, etc., feeding the quantitative analysis results back into the design parameter optimization process [44,45,46], thereby bridging the gap between user needs and product functionality [47]. In practice, some studies have used the SAPAD-FQFD model, particle swarm optimization (PSO), and RF algorithms to promote the coordination of user needs with product sustainability [48]. The CNN model was used to analyze and assess the posture of elderly individuals, achieving an accuracy of 96% and a precision of 98% [49]. Multi-frame and multi-path CNNs are used to assess ergonomic risks under occlusion conditions to ensure workplace health and safety [50].
The aforementioned studies demonstrate that the application of AI in preference prediction and emotion-driven design is guiding the transformation of industrial design from functional optimization to more emotional, personalized, and sustainable directions.

4.1.3. Process and Material Performance

AI drives progress in the fields of process and material design, contributing to the development of sustainable industries [51]. By combining explainable artificial intelligence (XAI) and boosting algorithms, the key process parameters are accurately identified using Shapley Additive Explanations (SHAP), and their optimal control ranges are determined [52]. In terms of process performance, the ANN has been shown to be a candidate model for accurate and efficient modeling of complex thermal systems, with a coefficient of determination of up to 0.9999 [53]. The application of ANNs and genetic algorithms in multi-objective optimization strategies enabled researchers to select the location of finned tubes on heat storage tanks, increasing the melting efficiency by more than 25%, achieving a significant breakthrough in the process performance of phase-change energy storage systems [54].
In terms of material performance, AI plays a key role in material design, performance evaluation, and material parameter optimization [55]. Relevant studies have widely applied AI in biochar adsorption performance [26], cellulose fiber mechanical property evaluation [56], material safety assessment [57], and compressive strength prediction of geopolymers and polymer concrete [58,59]. Notably, the above precise predictions provide a scientific basis for process optimization and environmental protection. For example, the Box-Behnken experimental design, an ANN, and support vector regression (SVR) were used to optimize the Fenton oxidation process [60]. Together, these efforts drive the development of industrial design in green and intelligent directions.
In summary, AI technology is being widely applied in industrial design, from design optimization to product preference prediction and process material performance optimization, promoting a shift in industrial design in a green and sustainable direction (Figure 8).
RQ3: At the system level, how does AI facilitate the integration and optimization of core systems such as design, production, and recycling to construct a holistic, sustainable paradigm?

4.2. System Level: AI-Driven Design Process and Production Optimization

4.2.1. Process Systematization

AI drives the sustainable development of industrial design by intelligently powering core processes such as design, production, recycling, and supply chains [61]. In the design process, AI and generative AI promote personalized product design and manufacturing [62,63]. For example, real-time fashion systems connect consumers, designers, and production, creating a data-driven closed loop from personalized design to manufacturing [39,64]. In the production process, AI enhances the efficiency and sustainability of production workflows through modeling, control, and decision-making processes [65]. Process applications aim to optimize production workflows, identify bottlenecks, and dynamically reconstruct systems using predictive models and real-time simulations [66]. In the recycling process, intelligent recycling, disassembly, remanufacturing decisions, and product lifespan extension are applied [31,67,68]. In supply chain processes, AI facilitates efficient resource allocation and agile response across organizations through demand forecasting, logistics optimization, inventory management, and supplier collaboration [69]. AI and the cyber–physical–social systems (CPSS) driven by industrial digitalization provide methodological support for the sustainable development of process industries [70]. However, ethical considerations in the transformation process remain limited and require further exploration [29,71].

4.2.2. Intelligent Production Systems

AI applications in intelligent production systems focus primarily on four areas: production system optimization [72], process intelligence, defect detection, and safety decision-making. In production system optimization, generative modeling techniques such as Transformer, generative adversarial networks (CGANs), diffusion models, and large language models (LLMs) [73,74] have surpassed traditional rule-based automation, effectively improving system efficiency and reducing energy consumption. In process intelligence, precision AI systems enable real-time monitoring and control of production environment parameters [75]. In defect detection within production processes, CNN-based automated visual inspection systems have successfully replaced manual inspection, improving defect detection accuracy [76]. In safety decision-making, deep learning-based intrusion detection systems have enhanced the protective capabilities of SCADA systems [77]. Despite their significant potential, emerging technologies such as LLMs still require further exploration regarding their applicability and implementation pathways in industry. The widespread adoption of these technologies faces multiple challenges, including high computational demands, data security, and integration with traditional systems [72,78].

4.2.3. Circular Economic System

Circular economy provides a core framework for achieving resource sustainability and low-carbon development in industrial systems by constructing product-level closed loops and symbiotic industrial networks [68]. The concept has been deeply integrated into the entire industrial design process, with specific applications including green product design [67,79], low-carbon material selection [80], component manufacturing, sustainable supply chain management [23,24], product lifespan extension [68], and the recycling of renewable energy battery systems [81]. To systematically promote circular transformation, researchers have proposed the application of high-performance AI computing algorithms, such as intelligent hardware design, non-parametric machine learning, and intelligent recognition algorithms, in the development of thermal energy cycle monitoring models [82]. AI, general artificial intelligence (GAI), big data analysis, and machine learning-driven smart product production (SPP) have been utilized to create environmentally friendly products aligned with circular economy principles [62]. To optimize resource flow, research advocates providing end-of-life knowledge at the BOL stage and integrating the entire lifecycle process through an EOL-to-BOL closed-loop information system [83].
In summary, the systematic application of AI technology in industrial design has been increasingly integrated, driving the transformation of industrial design from the traditional linear model to a data-driven and circular economy-oriented sustainable paradigm, including core processes such as design optimization, intelligent production, recycling, and supply chains (Table 4).

4.2.4. Supply Chain Management

AI is widely applied to core supply chain management systems, focusing on sustainable management, transportation network optimization, and performance management [86]. In sustainable supply chain management, the RTFS supply chain framework integrates Information and Communication Technology (ICT), AI, and virtual environments to reduce intermediate distribution links and enhance resource efficiency [64]. In transportation network optimization, AI-driven algorithms and decision models provide effective solutions to complex logistics problems. For example, a fuzzy bi-level decision support system (DSS) is used to optimize multimodal transport networks for perishable products [69]. An improved multi-objective artificial bee colony algorithm is used to design a multi-distribution channel network (MDCSCN), enhancing the intelligence of order allocation [27]. In performance management, AI and similar technologies must achieve an 80% adoption rate to significantly enhance sustainability performance [86]. The CAB2IN model provides a theoretical framework for conducting research on digital sustainable supply chain management from a circular economy perspective [23].
Therefore, at the system level, AI facilitates the transformation of industrial design into a data-driven, circular, and sustainable paradigm. This is achieved by integrating and optimizing core systems: the systematization of processes to establish end-to-end data loops, driving intelligent production to enhance resource efficiency and operational flexibility, supporting the circular economy to close resource loops, and optimizing supply chain management to improve responsiveness and sustainability. Collectively, these changes not only enhance individual process efficiency but fundamentally reorient industrial design towards greater intelligence and sustainability.
RQ4: At the institutional level, what are the key policy guidance needs, ethical challenges, and governance framework issues faced by the application of AI?

4.3. Institutional Level: Policy Guidance and Regulatory Roles

4.3.1. Environmental Policy

The global sustainable development agenda provides a top-level framework for policy formulation. The 17 Sustainable Development Goals (SDGs) proposed by the United Nations Environment Programme (UNEP) serve as a benchmark for global sustainable transformation [60]. Under the sustainable development framework, Europe has formed a policy cluster through the European Green Deal, the European Chemicals Sustainability Strategy for Sustainability (EC-CSS), and the Zero Pollution Action Plan, systematically guiding industries towards climate neutrality and a toxin-free environment. EC-JRC has published several reports on safe and sustainable design (SSbD) [57], incorporating green innovations in chemicals, materials, and products. In addition, the Circular Economy Action Plan (2015) and the new circular economy action plan (2020) clearly state that “digital technologies such as the Internet of things, big data, blockchain and artificial intelligence will accelerate the cycle” [71].

4.3.2. Ethical Norms

The risks of AI in industrial design ethics primarily focus on four dimensions: data privacy, algorithmic bias, environmental awareness, and social sustainability. Regarding data privacy and security issues, AI poses risks of excessive data collection and information inference, potentially leading to the leakage of sensitive personal information and severe consequences [29]. In terms of algorithmic bias, models trained on historical data are prone to discriminating against specific groups in services such as dynamic pricing and personalized recommendations. Moreover, the “black box” nature of algorithmic decision-making exacerbates the difficulty of accountability and traceability [87]. Regarding environmental awareness, due to the insufficient understanding of complex ecosystems, relying on AI for single-objective optimization (such as material reduction or energy efficiency improvements) may obscure hidden environmental costs across the product’s entire lifecycle and even lead to systemic risks such as pollution transfer. A particularly noteworthy aspect is the social sustainability dimension, where current research focuses on economic and environmental sustainability, neglecting to examine AI technologies in the broader context of social transformation and resilience. Therefore, a comprehensive framework and guidelines are needed to address regulatory and ethical issues [83]. Additionally, users need to understand their obligations and use AI technologies responsibly [62].

4.3.3. Policy Guidance

Government policies are a key social driver in promoting industrial energy efficiency, emission reduction, and sustainable development [70]. In the context of the global transformation of production and consumption patterns, global stakeholders (governments, policymakers, consumers, industry, academia, etc.) are increasingly focusing on the production and consumption of environmentally friendly products [46]. Many industrialized countries have set benchmarks for renewable energy, promoting the reuse of industrial energy [17]. For example, policies such as the EU’s “Horizon 2020” and China’s “Advanced Intelligent Remanufacturing Action Plan (2018–2020)” clearly define the responsibilities of producers throughout the product lifecycle [57,85]. The extended producer responsibility (EPR) system has demonstrated effective operational results in some regions [85]. It provides a clear institutional framework and compliance requirements for remanufacturing design practices while encouraging companies to reassess their production methods to meet environmental standards. Relevant studies suggest that governments should establish a systematic policy support framework, including fiscal incentives, financial support, and technology promotion, to achieve the green transformation of industries [88]. Policy design should place greater emphasis on systematically aligning with sustainability performance goals [17]. However, regulatory coordination still faces challenges. Insufficient regulatory coordination at the individual, corporate, local, national, and international levels, as well as the awareness and cost limitations of small and medium-sized enterprises, severely restrict the large-scale application of technologies [89] (Table 5).

5. Discussion, Challenges, and Future Research Directions

5.1. Discussion

This study systematically reviewed the literature on AI in the field of sustainable industrial design from 2015 to 2025. By identifying key journals and high-impact articles, a “technology–system–institution” framework was constructed, effectively tackling the core issues of the research.
The bibliometric analysis indicates a significant trend of growth in interdisciplinary research on AI and sustainable industrial design. Before 2022, the number of related publications was relatively limited, but between 2022 and 2025, this number increased. This trend not only reflects a sharp increase in attention paid to the intelligent transformation of industrial design but also highlights the widespread recognition of AI as a key technology for achieving sustainable development goals in industry. Furthermore, co-occurrence analysis reveals a clear distribution pattern in current research on AI and sustainable industrial design: “technology-driven, system-secondary, and institutionally weak.” Approximately 52% of the literature focuses on technological applications, 33% focuses on system integration, and only 5% explicitly addresses the institutional dimension. This result, to some extent, reveals the core challenges in this field: insufficient system integration and lagging institutional development. Specifically, rapid technological advancements are primarily focused on optimizing single aspects or localized problems, failing to also drive the systematic construction of cross-system collaborative mechanisms and institutional frameworks, and exacerbating the gap between research and practice. In terms of journal distribution, research findings are highly concentrated in top-tier journals in the field, such as Advanced Engineering Informatics, Sustainability, and the Journal of Cleaner Production, indicating high research quality and academic recognition. In terms of citation impact, the study by Wu et al. (2023) (TC: 354) [22] has the highest number of citations, with the studies by Liu et al. (2023) (TC: 316) [23] and Gebhardt et al. (2022) (TC: 224) [24] following behind. These studies provide an important theoretical and empirical reference for industrial design from a digital empowerment perspective.
The unique contribution of this review is that it proposes an interdisciplinary technology–system–institution integration perspective. Existing reviews focus on AI technology [18], classification [92], methods, and specific applications in the context of Industry 4.0 [19,93], and there is a lack of systematic research on the combination of AI technology with Sustainable Development Goals and a collaborative governance framework. This review fills this gap by focusing on how AI can effectively promote sustainable industrial design under the support and constraints of technology, systems, and institutions. At the technical level, previous studies have reviewed the current state of AI in industrial design, identifying key application areas such as design and process optimization and predictive maintenance [6,21]. This study further explores the applications of AI in various fields, including design optimization, product preference prediction, and process material innovation, from a technological perspective, showing that AI-driven generative design methods, when combined with big data, deep learning, and other technologies, facilitate information and data processing operations, ultimately achieving the shared goal of sustainable product production. Specifically, designers can leverage AI to support design through sentiment analysis and user profiling. In this process, designers incorporate their creativity and originality through iterative workflows, resulting in the completion of the design [62]. This transformation is driving a shift in industrial design from traditional functional optimization to emotional and personalized sustainable design. This shift not only addresses consumers’ emotional needs but also promotes the achievement of sustainability goals [46].
Existing research has confirmed the role of AI in optimizing the design process and application throughout the whole lifecycle of the equipment. However, research on how AI achieves systematic collaboration at the process industry level remains insufficient. This study reveals the profound transformation in sustainable industrial design facilitated by AI through process systematization, intelligent production, supply chain management, and the development of circular economy systems. It has been demonstrated that AI can integrate the design, manufacturing, management, and recycling stages into a collaborative and efficient organic whole, facilitating a shift from linear production models to data-driven, closed-loop value creation models. This study further highlights that system optimization relies on the collaboration and integration of cross-stage knowledge and emphasizes the extensive cooperation between various stages and stakeholders in the design process. However, existing industrial design practices lack effective digital solutions to seamlessly connect the design and production stages, particularly in the information flow and collaboration between different stages of the product lifecycle, such as the initial and end-of-life phases. This issue highlights the insufficient flow of information and data across departments, which hinders the sustainability and circularity of product design [83].
At the institutional level, the decisive role of policies and regulations in guiding and regulating the development of AI is emphasized by the findings of this study. The European Green Deal, the European Chemicals Sustainability Strategy for Sustainability (EC-CSS), the Zero Pollution Action Plan, and similar regulations, along with numerous policies introduced by governments worldwide, have encouraged and guided companies towards focusing on sustainable development, thereby creating a regulatory environment for the application of AI in sustainable design [57]. At the same time, the widespread application of AI has generated new institutional demands, such as requirements for algorithm transparency, data privacy, social responsibility, and ethical considerations [84,94]. To enhance data privacy and security, federated learning, as a distributed learning paradigm for privacy protection, has gained widespread attention [74]. However, lagging policy development remains one of the major challenges for the sustainable transformation of industrial design.
Despite the systematic procedures adopted in this review, several potential sources of bias in the study selection process should be acknowledged. First, the restriction to Scopus and Web of Science databases may have led to database bias, potentially excluding relevant studies indexed elsewhere or in grey literature. Second, the inclusion of English-language publications only may have introduced language bias. Third, although predefined keywords and inclusion criteria were applied, subjective judgment during screening and thematic coding may have influenced study selection. These factors may affect the completeness of the reviewed evidence and should be considered when interpreting the findings.
RQ5: What are the challenges and future directions for AI in sustainable industrial design within an integrated technological, system, and institutional framework?

5.2. Challenges

Although AI has great potential for promoting the sustainable development of industrial design, the research described in this paper still has certain limitations at the technical, systematic, and institutional levels. At the technological level, the controllability and interpretability of AI are insufficient [13]. Generative AI models struggle to fully capture complex semantic information, such as cultural and artistic aspects, during the product design process. This leads to discrepancies between the output and the design intentions, affecting the product’s uniqueness and market appeal. Additionally, high-quality and representative training data remain scarce, and the acquisition cost is high, limiting the training and generalization capabilities of AI models [77,95]. Furthermore, the “black box” nature of many generative architectures presents significant challenges to the interpretability and reliability of AI in safety-critical industrial design applications [29,66,96].
At the system level, the lack of cross-system integration and collaboration mechanisms remains a significant challenge [66,80]. Although AI technology has made some progress in various industrial processes, such as design, manufacturing, and recycling, current applications have not yet achieved full cross-system integration [23]. Specifically, in smart production systems and circular economy systems, the flow of data between different stages and collaboration across systems are still insufficient. This limits optimal resource allocation and hinders the effective implementation of a circular economy [83]. The fundamental reason lies in the lack of cross-domain unified data standards and the lack of open-system integration architecture; this has become a key bottleneck that hinders the large-scale popularization of AI technology and the construction of an integrated, sustainable industrial ecosystem.
At the institutional level, the delayed implementation of policy tools represents a significant challenge in the sustainable transformation of industrial design. As technology rapidly advances, existing policy frameworks are struggling to keep pace with the widespread application of AI in industrial design. This policy disconnect not only creates regulatory uncertainty for businesses applying AI but also diminishes the effectiveness of policies in guiding ethical compliance and fostering sustainable innovation. The extensive use of AI in industrial design has raised compliance risks regarding data privacy, algorithm transparency, and social responsibility, yet updates to the relevant regulatory and ethical guidance are notably delayed [71,97]. This phenomenon highlights the need to address the lag between policy and technology to ensure the sustainable development of AI applications.

5.3. Future Research Directions

To address the challenges outlined above, future research should focus on the following key areas (Figure 9):
  • Technological aspects: Future research should focus on enhancing the controllability and interpretability of AI models, particularly in capturing complex semantics such as culture and art, to improve the reliability and market adaptability of generative design.
  • Systemic aspects: Future research should strengthen data flow and collaboration mechanisms across systems, particularly by promoting the unification of cross-domain data standards. Additionally, an open system architecture should be established to facilitate collaborative work across various stages.
  • Institutional aspects: Future research should focus on developing policy frameworks that align with the rapid development of AI, bridging the gap between policy and technology. Furthermore, it should address compliance issues related to data privacy, algorithm transparency, and other key areas.
  • Integrative aspects: Future research should move beyond single-dimension analyses and explicitly examine the co-evolution and feedback mechanisms among technological innovation, system integration, and institutional governance in order to better support the large-scale and sustainable adoption of AI in industrial design.

6. Conclusions

In the past decade, research on AI in the field of sustainable industrial design has exhibited significant growth, especially from 2022 to 2025, marking this interdisciplinary field as a frontier in academic research. Despite the rapid growth in the number of studies, the current research landscape still exhibits clear fragmentation. Quantitative analysis shows that 52% of studies focus on technology application and 33% on system integration, with only 5% explicitly exploring the institutional dimension. This pattern of “Technology-led, followed by the system, weak institutions” reflects the lack of a systematic framework that can effectively integrate multi-dimensional research, thus restricting the development of theory and practice in this field.
To address this gap, this review develops a three-dimensional technology–system–institution framework to synthesize existing studies through a systematic literature review, providing an in-depth analysis of AI’s applications in sustainable industrial design from three perspectives. At the technological level, AI shows great potential in product design optimization, user preference prediction, and process material innovation. At the system level, AI enables the coordinated evolution of design, production, recycling, transportation, and supply chain processes by promoting process systematization, intelligent production, and circular economy models, though persistent challenges remain in cross-system integration. At the institutional level, environmental regulations provide policy guidance for the application of AI, while the technological characteristics of AI have also given rise to new requirements, such as for algorithm transparency and data ethics considerations, fostering an interdependent relationship between technology and policy.
By proposing this integrated model, our review offers a structured lens to consolidate previously fragmented insights. It should be noted that this synthesis is primarily based on publications from Scopus and Web of Science; although this ensured a focus on peer-reviewed work, future reviews could broaden their sources to enhance comprehensiveness. Future research could focus on challenges such as the interpretability and controllability of AI algorithms, the development of standards for cross-system data integration, and the creation of flexible policy frameworks to adapt to the continuous development of AI technologies. In doing so, key pathways could be provided for developing a new paradigm of industrial design that is efficient, inclusive, and sustainable.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr14050779/s1, Table S1: PRISMA 2020 checklist. Reference [98] is cited in the Supplementary Materials.

Author Contributions

Conceptualization, X.L. and C.L.; methodology, X.L. and J.Z.; data curation, K.L.; writing—original draft preparation, X.L. and J.Z.; writing—review and editing, Y.Z. and C.L.; validation, K.L.; supervision, Y.Z. and C.L.; project administration, Y.Z. and C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Anhui University Innovation Group: Clean Energy Key Technology and Equipment Innovation Team (Project No. 2023AH010055), the Anhui Provincial Scientific Research Plan Preparation Project (Project No. 2024AH053175), Anhui University Teaching Team “Mechanical Engineering Teaching Innovation Team” (Project No. 2024cxtd138), and the Major Scientific Research Projects of Anhui Provincial Department of Education (Project No. 2023AH040309).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The process of data collection follows the PRISMA framework.
Figure 1. The process of data collection follows the PRISMA framework.
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Figure 2. Published articles by year.
Figure 2. Published articles by year.
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Figure 3. Research directions of published papers.
Figure 3. Research directions of published papers.
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Figure 4. Papers published by subject area.
Figure 4. Papers published by subject area.
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Figure 5. Co-occurrence map.
Figure 5. Co-occurrence map.
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Figure 6. Visualization of co-occurrence overlay.
Figure 6. Visualization of co-occurrence overlay.
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Figure 7. Research framework of technology, system, and institution.
Figure 7. Research framework of technology, system, and institution.
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Figure 8. AI in sustainable applications for industrial design [34,35,41,52].
Figure 8. AI in sustainable applications for industrial design [34,35,41,52].
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Figure 9. Challenges and future research directions.
Figure 9. Challenges and future research directions.
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Table 1. Study inclusion and exclusion criteria.
Table 1. Study inclusion and exclusion criteria.
CriteriaPrinciples
Inclusion criteriaStudies published between 2015 and 2025 that are accessible online.
Articles discussing AI application to the sustainability of industrial design.
The article discusses at least one AI technology.
Exclusion criteriaNon-English-language articles.
Duplicate or extended papers of the same study (only the most recent version was selected).
Surveys, interviews, position papers, and comment papers were excluded.
Articles in a field other than industry, such as medicine or physics.
Table 2. List of journals publishing research on AI in sustainable industrial design.
Table 2. List of journals publishing research on AI in sustainable industrial design.
Journal TitlePublisher NameTotal
Publications		WoS Impact
Factor		Scopus
CiteScore
Advanced Engineering InformaticsElsevier89.913.1
SustainabilityMDPI63.37.7
Journal of Cleaner ProductionElsevier51020.7
Applied SciencesMDPI42.55.5
Expert Systems with ApplicationsElsevier37.515.0
International Journal of Advanced Manufacturing TechnologySpringer33.15.9
Journal of Intelligent ManufacturingSpringer37.416.5
Thermal Science and Engineering ProgressElsevier35.47.3
AI and SocietySpringer24.79.8
Ain Shams Engineering JournalElsevier25.912.2
Table 3. Most cited publications.
Table 3. Most cited publications.
TitleTCPublication YearSource
Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption3542023Wu et al. [22]
Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies3162023Liu et al. [23]
Industry 4.0 technologies as enablers of collaboration in circular supply chains: a systematic literature review2242022Gebhardt et al. [24]
Artificial intelligence-enabled environmental sustainability of products: Marketing benefits and their variation by consumer, location, and product types2092021Frank [25]
Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning1792022Zhu et al. [26]
Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels1502016Zhang et al. [27]
Component design optimization based on artificial intelligence in support of additive manufacturing repair and restoration: Current status and future outlook for remanufacturing1442021Abd Aziz et al. [28]
Modeling barriers of digital manufacturing in a circular economy for enhancing sustainability1332022Bag et al. [29]
Artificial intelligence in industrial design: A semi-automated literature survey1242022Tsang and Lee [30]
Applications of Industry 4.0 digital technologies towards a construction circular economy: gap analysis and conceptual framework1222022Elghaish et al. [31]
Notes: TC = total citations.
Table 4. Tools and applications for system optimization.
Table 4. Tools and applications for system optimization.
TypeTechnologiesApplicationReference
Intelligent design systemRadial basis function artificial neural network (RBF ANN), genetic algorithms (GAs), probabilistic neural network (PNN), and support vector regression (SVR)Sustainable and personalized clothing production[39]
Intelligent production systemDigital twin, industrial Internet of Things (IIoT), big data analytics, machine learningOptimization of manufacturing processes in the automotive industry[84]
Intelligent Circular SystemsMachine Learning (ML), Multi-objective Optimization Algorithms, decision support system (DSS), generative design, and Data Mining TechnologiesOptimization of product design, recycling, and remanufacturing decisions within closed-loop supply chains[85]
Decision-support system of supply chainFuzzy weighted goal programming (FWGP), possibilistic linear programmingSustainable supply chain and transportation network configuration, particularly for perishable product distribution optimization[69]
Table 5. Sustainable development policy in industrial design.
Table 5. Sustainable development policy in industrial design.
TypePolicyApplicationChallengesReference
Environmental PolicyEU’s “Green Deal”Promoting sustainable materials, product innovation, and lifecycle assessmentNeed to ensure alignment between policy execution and business practices[57]
Ethical NormSocial Failure Mode and Effects Analysis (SFMEA)AI was used to identify social risks (such as health and safety) in product design, and social responsibility dimensions were integrated into the design processOver-reliance on technology (e.g., ChatGPT) that lacks ethical reasoning ability[90]
Policy GuidanceAdaptive Governance FrameworkPromoting energy conservation, emission reduction, and industrial green transformationSmall and medium-sized enterprises face blind spots in energy efficiency improvement and limited technology adoption[91]
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MDPI and ACS Style

Li, X.; Zhang, Y.; Liu, C.; Zhao, J.; Li, K. Artificial Intelligence for Sustainable Industrial Design: A Systematic Literature Review Based on a Technology–System–Institution Framework. Processes 2026, 14, 779. https://doi.org/10.3390/pr14050779

AMA Style

Li X, Zhang Y, Liu C, Zhao J, Li K. Artificial Intelligence for Sustainable Industrial Design: A Systematic Literature Review Based on a Technology–System–Institution Framework. Processes. 2026; 14(5):779. https://doi.org/10.3390/pr14050779

Chicago/Turabian Style

Li, Xinyu, Yingyan Zhang, Conghu Liu, Junyi Zhao, and Kai Li. 2026. "Artificial Intelligence for Sustainable Industrial Design: A Systematic Literature Review Based on a Technology–System–Institution Framework" Processes 14, no. 5: 779. https://doi.org/10.3390/pr14050779

APA Style

Li, X., Zhang, Y., Liu, C., Zhao, J., & Li, K. (2026). Artificial Intelligence for Sustainable Industrial Design: A Systematic Literature Review Based on a Technology–System–Institution Framework. Processes, 14(5), 779. https://doi.org/10.3390/pr14050779

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