Leveraging Machine Learning in Next-Generation Climate Change Adaptation Efforts by Increasing Renewable Energy Integration and Efficiency

Abstract

This article examines the growing role of machine learning (ML) in promoting next-generation climate change adaptation through the improved integration and performance of renewable energy systems. As climate change accelerates, innovative solutions are urgently needed to enhance the resilience and sustainability of energy infrastructure.ML offers powerful capabilities to handle complex data sets, forecast energy supply and demand, and optimize grid operations. This review highlights key applications of ML, such as predictive maintenance, intelligent grid management, and the real-time optimization of renewable energy resources. It also examines current challenges, including data availability, model transparency, and the need for interdisciplinary collaboration, both in technology development and policy and regulation. By synthesizing recent research and case studies, thisarticle shows how ML can significantly improve the performance, reliability, and scalability of renewable energy systems. This review emphasizes the importance of aligning technological advances with policy and infrastructure development. Successful implementation requires not only ensuring technological capabilities (robust infrastructure, structured data sets, and interdisciplinary collaboration) but also the careful consideration and alignment of ethical and regulatory factors from strategic to regional and local levels. Machine learning is becoming a key enabler for the transition to more adaptive, efficient, and low-carbon energy systems in response to climate change.

1. Introduction

Climate change is one of the greatest challenges of the 21st century, requiring urgent and innovative approaches to reduce greenhouse gas emissions and increase environmental resilience [1]. Renewable energy sources such as solar, wind, and hydropower offer sustainable alternatives to fossil fuels and are increasingly popular [2,3], but their integration into existing energy systems, e.g., in the form of energy cooperatives, poses technical and operational challenges [4,5]. Supply variability (day/night, seasons, and weather conditions), demand forecasting (during the day, weekly, and seasonally), and grid management, as well as energy storage, are among the critical obstacles to the widespread deployment of renewable energy sources [6]. Scientists and engineers have been searching for a panacea for these problems for years; hence, machine learning (ML) has emerged as a powerful tool to address these complexities by analyzing large data sets, predicting patterns, and optimizing system performance [7,8,9,10]. The use of ML can significantly improve the efficiency, reliability, and scalability of renewable energy systems [11]. Climate change adaptation requires transformative strategies that go beyond emission reduction to ensure system resilience under changing environmental conditions. A critical area of intervention is the energy sector, where the integration and efficient use of renewable energy sources are key for both mitigation and adaptation purposes.However, the inherent variability and instability of renewable energy sources such as wind and solar create operational challenges for grid stability and reliability. ML as a data-driven approach offers a powerful set of tools to address these challenges through data-driven forecasting, optimization, and real-time decision-making. In particular, ML can improve the short-term and long-term forecasting of renewable energy production by learning complex patterns from weather and historical generation data. This predictive accuracy enables more efficient dispatch and storage strategies, reducing curtailment and dependence on fossil fuel-based backup systems. ML algorithms also facilitate dynamic load balancing by continuously adapting to real-time supply and demand fluctuations in grid operation. As climate impacts intensify—for example, through more frequent extreme weather events—these adaptive capabilities become increasingly critical. The role of ML is therefore not supportive but central to increasing the resilience and efficiency of next-generation energy systems. The focus on technically validated use cases underscores the practical feasibility of ML as a cornerstone in efforts to adapt to climate change through the integration of renewable energy.

In the context of using ML to adapt to next-generation climate change by increasing the integration and efficiency of renewable energy, the following definitions apply:

• Artificial intelligence (AI) refers to a broad area of computational systems that can perform tasks that typically require human intelligence, such as learning, reasoning, and decision-making. In energy systems, AI includes tools that support autonomous network operations, adaptive planning, and scenario analysis under climatic stressors.
• ML is a subfield of AI that focuses on algorithms that can learn patterns from data and improve performance over time without explicit programming. ML is particularly valuable in forecasting renewable energy production, optimizing energy distribution, and identifying adaptive control strategies in complex and variable environments.
• Deep learning (DL) is a specialized branch of ML that uses multi-layer neural networks to model highly complex and nonlinear relationships. In the energy context, DL is applied to tasks such as high-resolution weather forecasting and image-based infrastructure monitoring, where traditional models may not be able to capture complex relationships.
• Predictive analytics refers to the application of statistical and machine learning techniques to forecast future events or trends based on historical and real-time data. It is key to climate adaptation in energy systems, enabling the future-proof management of renewable generation, demand fluctuations, and grid stability under uncertain climatic conditions.

Together, these tools form a hierarchical framework in which AI encompasses ML, which in turn encompasses DL, and predictive analytics is a key functional application of these techniques in operational energy and climate planning.

This study aims to investigate how ML technologies can enhance climate change adaptation by supporting smarter integration and more efficient use of renewable energy [12]. Specifically, it explores ML applications in areas such as energy forecasting, smart grid optimization, and resource allocation [13]. The study also assesses the potential of ML to reduce system-level inefficiencies and emissions while ensuring energy equity and resilience. By combining technological innovation with environmental sustainability, the research contributes to the development of next-generation climate adaptation strategies [14,15,16]. Thus, the goal of this article is also to provide policymakers, researchers, and energy stakeholders with actionable insights to drive a low-carbon energy future using ML [17,18,19,20]. The validation of ML solutions using real-world data showed a 15% reduction in grid losses and a 22% increase in renewable energy use compared to traditional methods [21]. However, to fully realize this potential, regions should strengthen the potential of green finance, scale up the workforce, and adapt environmental regulation tools to increase the effectiveness of policies [22].

The term “next-generation climate adaptation” in this context does not strictly refer to the category defined by the Intergovernmental Panel on Climate Change (IPCC), but is consistent with the broader, evolving adaptation strategies emphasized in recent climate resilience frameworks, including, but not limited to, the IPCC Guidance. In particular, it refers to data-driven, technology-driven, and system-level approaches that go beyond traditional infrastructure reinforcement or reactive measures. These next-generation strategies integrate real-time decision-making, predictive analytics, and dynamic system optimization—all features enabled by technologies such as ML. In the energy sector, next-generation adaptation includes the following: forecast-based integration of renewables, where ML improves the predictability of variable sources such as wind and solar; adaptive grid management, where smart dispatch and control systems respond to climate-induced fluctuations in demand or generation; and decentralized and resilient energy systems, such as smart microgrids, that can self-regulate during climate disruptions. While the IPCC advocates “transformational adaptation” involving systemic change, the “next generation” label emphasizes the operationalization of such transformation through emerging digital technologies such as ML. Thus, this phrase positions ML not only as a supporting tool but as a key enabler of proactive, flexible, and high-resolution adaptation strategies within climate-sensitive energy systems.

This review highlights that the use of ML goes beyond the automation of energy generation and distribution systems—it represents a qualitative change in the way energy systems can adapt, learn, and evolve in sync with climate threats. The novelty and contribution lie in demonstrating large-scale operability, precision under uncertainty, and the ability to transform real-time data into continuous adaptation, which distinguish ML as a fundamental enabler of next-generation climate strategies. Unlike conventional approaches, ML can process vast amounts of heterogeneous data—such as weather inputs from satellites, network sensor data, and consumer demand patterns—to generate real-time adaptive insights. The combination of strategic, operational, and regional/local levels involves leveraging edge computing and federated learning to provide ML with the ability to continuously learn and self-update with new data, which is critical for operating in rapidly changing climate and energy environments. This includes reducing forecast lead times and accuracy for variable energy sources, directly reducing the need for fossil fuel-based reserves, and minimizing energy constraints. ML enables rapid, local adaptation necessary for climate resilience where conditions vary regionally and are subject to unpredictable fluctuations (e.g., sudden local cloud cover).

1.1. Genesis of the Issue

The genesis of ML for climate change adaptation began with a growing awareness that traditional energy systems lacked the flexibility and responsiveness required for variable renewable energy sources [23]. As solar and wind power became more popular, their intermittent nature posed challenges to grid stability, prompting the exploration of predictive analytics [24]. Early applications of ML focused on weather forecasting and energy demand prediction, laying the groundwork for smarter energy management systems. Researchers quickly realized that ML could also be used to optimize energy storage, load balancing, and real-time control, greatly improving system performance [25]. Advances in computing power and data availability enabled deep learning and reinforcement learning to more accurately model complex energy systems. The urgency of climate change, coupled with international climate agreements, drove funding and collaboration across the energy, AI, and environmental sciences sectors [26]. Governments and industries have begun to adopt ML for grid-level renewable energy integration, anomaly detection, and predictive maintenance, enhancing operational resilience [27]. ML has also found applications in energy-efficient city planning, building automation, and industrial process optimization, combining adaptation with sustainability [28]. The concept of next-generation climate resilience has emerged, with ML at its core, to enable adaptive, data-driven energy infrastructures [28]. The use of ML is seen not only as a technological advance but as a strategic imperative to accelerate the deployment of renewable energy sources and build climate-resilient societies (Figure 1) [29].

ML offers concrete technical contributions to climate change adaptation by significantly improving renewable energy forecasting accuracy, as seen in DeepMind’s wind forecasting system. Improved forecasting enables better grid planning and storage management, reducing inefficiencies and increasing renewable energy utilization. Intelligent dispatch platforms, such as those used by China’s National Grid, demonstrate how ML can operationally optimize real-time load balancing to adapt to renewable energy variability. However, maximizing these benefits requires policy measures that support standardized data sharing across energy, weather, and infrastructure domains to ensure model interoperability and continuous improvement. Governments should also establish interdisciplinary collaborative frameworks that mandate coordination between AI researchers, grid operators, and climate scientists for targeted system design. Together, these technology- and institution-based strategies create a workable path for ML to directly support next-generation climate-resilient energy systems.

1.2. Scientific, Economic, and Social Gaps

A key research gap lies in the limited availability of high-quality, real-time data needed to train ML models for accurate energy forecasting and grid optimization. Many ML models are not generalizable across geographies and energy systems, making them less effective in global climate change adaptation contexts [30]. There is a lack of sufficient research on interpretable and transparent ML models, which hinders trust and adoption in critical infrastructure sectors such as energy [31]. The integration of climate science and ML is still in its infancy, resulting in models that may not adequately account for long-term climate variability or extreme events [32]. Scientific collaboration among energy engineers, data scientists, and climate experts is often fragmented, limiting holistic solutions [33]. On the economic front, there is a lack of cost-effective models for implementing ML, especially in low-income regions and small utilities that face infrastructure and financial constraints [34]. The return on investment (ROI) of ML-based solutions for renewable energy integration is often uncertain, discouraging private sector involvement. Economic policies and energy markets are not yet designed to encourage ML-based optimizations, creating regulatory bottlenecks [35]. The upfront costs of digital infrastructure (sensors, edge devices, and data platforms) are a barrier to widespread adoption of ML in renewable systems [36,37]. There is a significant digital literacy gap in society, particularly in rural and developing areas, which limits the ability of the workforce to engage in ML-enabled energy systems [38]. Public awareness of the role of ML in climate change adaptation is low, reducing public support and funding for related innovation [39]. Privacy and data security concerns related to energy consumption data can reduce willingness to share or use data in ML applications [40]. Communities may resist ML-based automation due to fears of job losses in traditional energy sectors [41]. Biases in ML models, if not addressed, can lead to the unequal distribution of energy resources or adaptation benefits across different social groups [42]. There is a need for inclusive policy frameworks that ensure that ML solutions for renewable energy and climate change adaptation are fair, transparent, and community-based (Figure 2) [43,44].

This paper is a conceptual review rather than a validation of the applied model, focusing on the strategic role of ML in promoting next-generation climate change adaptation through renewable energy integration and efficiency. Its aim is to synthesize existing technical pathways, use cases, and institutional enablers where ML contributes to adaptive energy system resilience. The scope includes predictive modeling for renewable generation, smart grid distribution, and system optimization under climate variability, but does not test or validate specific ML algorithms. By examining both technical and policy dimensions, this review explains how ML capabilities align with evolving climate change adaptation needs, especially in energy infrastructure. This conceptual framework provides a basis for identifying feasible research gaps and policy mechanisms that support the operational implementation of ML in climate-adaptive energy systems.

2. ML Applications in Renewable Integration

2.1. Data Set

This bibliometric analysis was conducted to examine the state of research and the state of knowledge and practice in the area of planning and implementing ML to optimize adaptation to climate change by increasing the integration and efficiency of renewable energy. In order to efficiently carry out this analysis, commonly known and used bibliometric methods were used to analyze current, recently published (i.e., up to 10 years ago) scientific publications with a global reach. This approach assumes formulating such research questions that, thanks to the answers to them, it is possible to identify key areas included in the current state of research, the origin of the publications (institutions, country, and sources of research funding), the most influential authors (as leaders of research teams) and articles, and also—if possible—the evolution of research topics in recent times.This is important due to the observed dynamic changes in conducting and financing research in the area of AI, green technologies, and sustainable development. In addition, when possible, an attempt was made to identify SDGs related to the publications included in the review, as part of the global transformation that prepares us for the world in 2030 with respect for the environment and future generations. This approach allows for a more comprehensive understanding of current research and economic and social trends, strategies, and research and business practices based on the use of ML in climate change adaptation. It provides the necessary understanding and planning of further development activities in this field, strengthening its potential in both the technological and regulatory areas. Such interpretation of current and relevant bibliometric data enriches current discussions and provides a solid basis for future research and similar reviews.

2.2. Methods

In this study, a deliberate and planned search of four bibliographic databases was used: Web of Science (WoS), Scopus, PubMed, and dblp. This combination resulted in a search covering the widest possible scope of studies and a wealth of data of global importance for the development of knowledge and its applications (

Table 1. Bibliometric analysis procedure (authors’ own approach).

Name of Stage	Tasks
Defining research goals	Defining exact goals of the bibliometric analysis
Selecting bibliometric databases	Choosing appropriate data sets and developing research queries according to the study goals
Data preprocessing/preparation	Removing duplicates and irrelevant records from the collected data set; organizing the records to adapt it to the requirements of the ML training set
Bibliometric software selection	Selection of optimal tools from the area of bibliometric software for analysis
Data analysis	Description/keywords, type of publication, author, affiliation, area/topic, country, etc.
Analysis of results/visualization (where possible)	Presentation of the results to emphasize insights
Interpretation of resultsand discussion	Interpreting results in the context of the research goals

). In order to quickly identify leading results, appropriate filters were applied so that further analyses could focus only on selected studies, narrowing the search scope to articles in English. After filtering, each article was manually reviewed again individually to ensure that it met the inclusion criteria, which, once performed, allowed the final sample size to be determined. Then, the main features of the data set were analyzed, including the most frequent authors/research groups, institutions, countries, funding mode (if reported), scientific fields, and subject groups. This allowed us to map the main research achievements in the study area and identify emerging trends, which were not always in line with expectations. Where possible, we tracked temporal trends to monitor changes in the research area over time and grouped publications into thematic clusters that showed relationships across different research areas. This process highlighted important themes and subfields within the research area, including emerging ones.

In this study, selected elements of the PRISMA 2020 guidelines for bibliographic reviews were used to structure the research process and ensure its replication. The focus was on the following ten selected aspects of PRISMA 2020 presented in the Supplementary Material:

• Item 3:justification;
• Item 4: objectives;
• Item 5: eligibility criteria;
• Item 6: information sources;
• Item 7: search strategy;
• Item 8: selection process;
• Item 9: data collection process;
• Item 13a: synthesis methods;
• Item 20b: synthesis results;
• Item 23a: discussion.

For the bibliometric analysis, tools embedded in the Web of Science (WoS), Scopus, PubMed, and dblp databases were directly used. The selected review methodology supports the replication of the study, enabling the refinement of categorization by author, affiliation, keyword, research area, document, and source standardized in the above-mentioned databases. The results of the performed analyses are presented in a table to provide comprehensive and flexible analysis and visualization, adapted to the complexity of the topic.

3. Results

3.1. Data Sources

In order to refine the search in the selected databases, advanced queries were used using filters, limiting the results to articles in English. The search was performed as follows:

• In the WoS database, the “Subject” field (consisting of title, abstract, keywords plus, and other keywords) was used;
• In the Scopus database, the article title, abstract, and keywords were used;
• In PubMed and dblp databases, manual sets of keywords were used.

The databases were searched for articles using keywords such as “machine learning” or “ML”, “climate change” AND “energy efficiency” OR “energy integration” (

Table 2. Detailed database search query (authors’ own version).

Parameter/Feature	Detailed Description
Inclusion criteria	Books, book chapters, articles (original, reviews, and editorials), and conference proceedings, in English
Exclusion criteria	Articles, books, and chapters older than 10 years; letters; conference abstracts without full text; other languages than English
Keywords used	machine learning, climate change, energy optimization/optimisation
Used field codes (WoS)	“Subject” field (consisting of title, abstract, keyword plus, and other keywords)
Used fields (Sopus)	Article title, abstract, and keywords
Used fields (PubMed)	Manually
Used fields (dblp)	Manually
Boolean operators used	No
Filters used	Results were refined by year of publication, document type (e.g., articles or reviews), and subject area (industry, engineering, computer science, physics, etc.)
Iteration/validation option(s)	The query is used iteratively, refined in subsequent iterations based on the results, and verified by checking whether relevant publications appear among the top results
Wildcards and leverage truncation	The symbol * was used for word variations (e.g., “energ*” for “energy” or “energetic”) and the symbol? for alternative spellings (e.g., “optimi?ation”)

).

The selected set of publications was then further refined by manually re-screening the articles and removing irrelevant publications and duplicates to determine the final sample size.A summary of the bibliographic analysis results is presented in

Table 3. Summary of bibliographic analysis results (WoS, Scopus, PubMed, and dblp).

Parameter/Feature	Value
Leading types of publication	Article (32.20%), conference paper (30.50%), review (30.50%)
Leading areas of science	Engineering (24.80%), Computer science (16.30%), Energy (14.40%), Environmental science (8.50%)
Leading countries	India, USA, Saudi Arabia, United Kingdom
Leading scientists	Kumar, P.P.; Zhou, Y.
Leading affiliations	University of Johannesburg, Hongkong University of Science and Technology, Universite Ibn Tafail, Seoul National University
Leading funders (where information available)	National Natural Science Foundation of China, Ministry of Science and Technology of China, European Commission, National Research Foundation of China

. Eighty-five articles (published 2021–2025) were reviewed (older articles were not observed).

3.2. ML-Based Energy Forecasting, Smart Grid Optimization, and Resource Allocation

ML plays a key role in improving energy forecasting, smart grid optimization, and resource allocation, which are essential for integrating renewable energy into climate change adaptation strategies [45]. In energy forecasting, ML algorithms analyze historical weather, consumption, and generation data to predict future energy demand and renewable energy production with high accuracy. This predictive capability helps grid operators manage solar and wind variability, ensuring stable and efficient energy supply [46]. Advanced models such as deep learning and ensemble methods further enhance the reliability of forecasting over short and long-time horizons. In smart grid optimization, ML enables the dynamic control of distributed energy resources by learning from real-time data streams [47]. These systems can autonomously adjust energy flows, manage loads, and respond to fluctuations in supply and demand [48]. Reinforcement learning and neural networks are particularly effective in making decisions about grid stability and performance. ML also supports fault detection, equipment failure prediction, and transmission loss reduction, improving overall grid reliability [49]. In resource allocation, ML algorithms optimize energy distribution based on usage patterns, grid capacity, and environmental conditions [50]. This includes deciding when and where to store or dispatch renewable energy, thereby maximizing its utility and minimizing waste [51]. ML helps prioritize energy delivery to critical infrastructure and vulnerable communities, supporting equitable and resilient adaptation measures. It can also guide infrastructure investments by identifying high-impact areas for renewable energy deployment [52]. Together, these ML applications make energy systems more adaptive, efficient, and resilient to climate-related disruptions. They reduce dependence on fossil fuels, making renewable energy more predictable and manageable [53]. Importantly, ML facilitates decentralized energy solutions, empowering local communities and reducing systemic risk. However, successful implementation requires high-quality data, transparent models, and robust cybersecurity. Using ML in these areas is critical to achieving sustainable energy goals in the face of climate change (Figure 3) [54].

3.3. ML in Reducing System-Level Inefficiencies and Emissions

ML is making a significant contribution to reducing system-level inefficiencies and emissions in the energy sector, which is crucial for climate change adaptation. By analyzing large and complex data sets in real time, ML can identify patterns and anomalies that lead to energy waste [55]. For example, ML models can detect inefficiencies in energy transmission and distribution systems, allowing operators to take corrective action quickly. These insights lead to optimized network performance, minimizing energy losses during periods of peak demand or when renewable generation is variable [56]. ML also improves energy storage management by predicting optimal charge and discharge cycles, ensuring that supply is better matched to demand [57]. This minimizes the need for backup power from fossil fuel sources, directly reducing greenhouse gas emissions. In industrial and commercial settings, ML is used to monitor equipment and operational processes, identify energy-intensive operations, and suggest more efficient alternatives [58]. ML-assisted predictive maintenance prevents system failures and reduces downtime, further improving overall energy efficiency. In building management, ML algorithms control heating, cooling, and lighting systems based on occupancy patterns and weather forecasts, reducing unnecessary energy consumption [59]. Additionally, ML-based demand response systems encourage consumers to shift energy use to off-peak periods, reducing the load on power grids and lowering emissions during periods of high demand. In renewable energy systems, ML enables better integration and control of distributed generation resources, reducing curtailment and underutilization [60]. By optimizing logistics and supply chains in the energy sector, ML also reduces emissions associated with fuel transportation and infrastructure development. Additionally, ML models inform decision-makers and planners by simulating the environmental impacts of different energy scenarios, supporting low-carbon decision-making [61]. These capabilities help achieve more sustainable operations without compromising system reliability or efficiency. Importantly, ML contributes to a cleaner energy transition by increasing the scalability and efficiency of renewable energy sources [62]. However, realizing these benefits requires addressing data privacy, model transparency, and regulatory challenges. ML is a key enabler of reducing emissions and inefficiencies, paving the way for a more flexible and resilient energy future (Figure 4) [63].

3.4. ML in Ensuring Energy Equity and Resilience

ML plays an important role in promoting energy equity and resilience, which are essential for effective adaptation to climate change. By analyzing demographic, geographic, and consumption data, ML can help identify underserved or vulnerable communities [64]. This allows for targeted interventions, such as deploying microgrids or energy-efficient technologies where they are needed most [65]. ML also enables the equitable distribution of renewable energy by optimizing the deployment of solar panels, wind turbines, and storage systems based on local needs and environmental conditions. In terms of resilience, ML increases the ability of energy systems to withstand and recover from climate-related disruptions [65,66]. Predictive models can forecast extreme weather events and help energy providers take preventive measures to protect infrastructure and maintain service continuity. ML algorithms can also guide real-time responses to emergencies, prioritizing energy delivery to critical services such as hospitals, shelters, and first responders [67,68]. This capability shortens recovery times and minimizes the social and economic impacts of power outages. ML supports decentralized energy systems, such as community-based solar and storage solutions, which improve access and reduce dependence on centralized grids [69]. These systems are particularly valuable in remote or disaster-prone areas, increasing both resilience and equity. Additionally, ML-based demand response programs can be designed to address the needs of low-income households while avoiding the regressive effects of pricing schemes [70]. By ensuring that energy efficiency programs are inclusive, ML can help reduce energy burdens and improve the livelihoods of marginalized populations. ML also contributes to transparent and data-driven policymaking, ensuring that decisions reflect the diverse needs of communities and support environmental justice goals [71]. Through real-time monitoring and adaptive control, ML increases the reliability and responsiveness of renewable energy systems under stress. This strengthens the long-term sustainability of energy infrastructure in a changing climate [72]. However, care must be taken to prevent biases in ML models that could reinforce existing inequalities. If designed and implemented responsibly, ML is a powerful tool for advancing both equity and resilience in next-generation climate adaptation efforts [73].

3.5. Combining Technological Innovation with Environmental Sustainability Toward Next-Generation Climate Adaptation Strategies

Combining ML-based technology innovation with environmental sustainability is key to developing next-generation climate adaptation strategies. ML is improving the precision, speed, and scalability of renewable energy integration, making sustainable energy systems more responsive to climate variability [74]. By processing massive data sets—from weather patterns to energy consumption trends—ML enables accurate prediction and real-time optimization, reducing waste and improving efficiency [75]. This directly supports the sustainability goal of minimizing emissions while meeting growing energy demand. ML-based tools can guide the strategic deployment of renewable infrastructure, ensuring minimal ecological disruption and maximum societal benefit [76]. For example, ML models can identify optimal locations for solar and wind farms, taking into account environmental, economic, and social factors. In agriculture, ML supports sustainable practices by predicting water needs, optimizing irrigation, and reducing the carbon footprint of food production. These applications demonstrate how ML is adapting technological advances to protect the environment. Furthermore, ML facilitates the development of closed-loop energy systems by improving energy storage, recycling, and network connections [77]. In urban planning, ML helps design smart, low-carbon cities with efficient transport, energy, and waste systems. ML also enables the continuous monitoring of environmental indicators, enabling adaptive policy responses that reflect real-time conditions [78]. This integration of data and actions is essential for dynamic climate resilience. When embedded in a policy framework, ML can drive sustainable innovation through incentives and compliance mechanisms [79]. However, responsible implementation is crucial—models must be transparent, fair, and free from biases that could undermine social and environmental goals [80]. Collaboration between technologists, environmental scientists, and policymakers is essential to provide holistic and equitable solutions. Using ML in a sustainability-oriented framework can accelerate the transition to resilient, low-carbon systems. This synergy creates the basis for proactive, intelligent, and inclusive adaptation to climate change (

Table 4. ML methods vs. application domains.

ML Method	Application Domain	Strengths	Limitations
Linear regression	Solar/wind generation prediction	Simple; interpretable	Poor performance on nonlinear patterns
Random forest/ XGBoost	Energy demand forecasting; grid anomaly detection	High accuracy; interpretable	Works with limited data sets; can overfit without tuning; less effective on sequential data
LSTM	Time-series prediction for renewable output	Captures long-term dependencies in weather or demand sequences	Requires bigger data sets; high training cost
CNNs	Satellite data analysis for solar potential mapping	Spatial pattern recognition	Data-intensive; complex to train
RL	Real-time grid dispatch; energy storage optimization	Learns adaptive control policies in dynamic environments	Slow convergence; exploration risk
SVMs	Fault detection in energy infrastructure	Effective in small data sets; good generalization	Limited scalability for large data sets

) [81].

3.6. Data Availability, Model Transparency, and the Need for Interdisciplinary Collaboration

Data availability, model transparency, and interdisciplinary collaboration are essential elements for effectively using ML to address next-generation climate change adaptation through renewable energy integration and efficiency. High-quality, real-time, and diverse datasets are essential for training accurate ML models that can forecast energy demand, optimize grid operations, and manage renewable variability [82]. However, data silos, privacy concerns, and inconsistent data standards often impede access, limiting model effectiveness and scalability. Open data platforms and shared data governance frameworks are key to ensuring equitable access and improving model robustness [83]. Model transparency is equally important, especially for high-impact applications such as energy distribution and disaster response. Many advanced ML techniques, especially deep learning models, are often considered “black boxes,” making it difficult for stakeholders to understand how decisions are made. Transparent, explainable ML models within eXplainable AI (XAI) build trust with decision-makers, energy providers, and the public, ensuring accountability and fairness. This transparency is also key to the regulatory compliance and ethical use of AI technologies in public infrastructure [84,85]. Cross-disciplinary collaborations connect data science, environmental science, engineering, and policy. Adapting to climate change requires integrated solutions that consider technical feasibility, environmental impact, social equity, and economic viability [85]. Cross-disciplinary collaborations ensure that ML models are not only technically sound but also contextually relevant and ethically sound. Energy planners, AI researchers, and climate scientists must work together to define goals, interpret results, and responsibly scale solutions [86]. Cross-sector engagement, including input from communities and civil society, can improve model design and ensure local needs are addressed (Figure 5). Joint efforts also accelerate innovation, enabling more adaptive and resilient energy systems. Shared expertise streamlines model validation, prevents duplication of effort, and supports the faster implementation of sustainable technologies [87]. Without this interdisciplinary foundation, ML applications risk being out of sync with real-world conditions and policy priorities. Data availability, model transparency, and interdisciplinary collaboration are the backbone of ethical, efficient, and impactful ML-based climate adaptation strategies [88,89].

4. Discussion

Integrating ML into renewable energy systems is a groundbreaking approach to enable resilient, efficient, and scalable adaptation to climate change. Using ML in next-generation climate change adaptation increases renewable energy integration and operational efficiency in energy systems [90]. ML algorithms can predict solar and wind power generation with greater accuracy, reducing dependence on fossil fuel backups [91]. By analyzing massive data sets, ML can predict energy demand in real time, enabling better load balancing and grid management [92]. Smart systems can optimize the deployment and operation of renewable energy infrastructure, such as solar panels and wind turbines, maximizing efficiency [93]. ML also improves the performance of energy storage systems by predicting usage patterns and battery performance [94]. In smart grids, ML improves demand response strategies by dynamically adjusting consumption based on real-time conditions [95]. ML-assisted predictive maintenance helps reduce downtime in renewable energy systems, increasing reliability and longevity [96]. Climate models enhanced with ML insights offer more detailed, localized forecasts, guiding targeted adaptation measures [97,98]. ML-based analytics support policymaking by identifying the most effective interventions based on historical and real-time data [88,99].

Different ML approaches offer different advantages in climate-resilient energy systems; for example, deep learning models such as LSTM are highly effective in forecasting renewable generation time series, while reinforcement learning is excellent for adaptive dispatch and control under uncertain grid conditions. However, deep models often require large data sets and large computational resources, making them less feasible in regions with limited digital infrastructure. In contrast, tree-based models such as XGBoost offer robust performance on smaller data sets and are more interpretable, which can be beneficial for regulatory clarity. Geographically, countries such as China benefit from centralized data access and vertically integrated grid systems, enabling uniform large-scale ML deployment, while decentralized markets such as the US or EU face interoperability challenges due to fragmented stakeholders and regulatory heterogeneity. Policy environments also play a key role: top–down mandates in China are accelerating ML integration, while in liberalized markets, adoption depends more on market incentives and public–private partnerships. These contrasts underscore that while ML is technically portable, its operational impact is highly dependent on data availability, institutional coordination, and regulatory context.

4.1. Scientific Consequences of Achievement

Leveraging ML in next-generation climate change adaptation efforts significantly advances scientific understanding and response strategies [89]. ML enhances predictive accuracy for climate models by analyzing vast, complex datasets, leading to more precise forecasting of extreme weather events. It optimizes renewable energy systems, such as solar and wind, by forecasting generation potential and adjusting operations in real time for maximum efficiency. Smart grid management benefits from ML through improved load balancing, fault detection, and demand forecasting, enabling greater integration of intermittent renewables. The development of adaptive energy storage strategies is accelerated as ML identifies optimal charging and discharging patterns based on usage and weather data. Scientific research in materials science is boosted by ML algorithms that predict the performance and degradation of renewable technologies under climate stressors. ML also facilitates large-scale simulations for evaluating the resilience of energy infrastructure under various climate scenarios [100,101,102]. The continuous feedback loop created by ML-driven monitoring systems contributes to dynamic, data-informed policy development [103]. Interdisciplinary collaboration increases as ML tools bridge domains like climatology, energy systems, and data science. ML catalyzes a new era of scientifically grounded, adaptive, and efficient climate resilience strategies [104]. Experience to date has shown that integrating ML into energy systems strengthens our ability to mitigate climate risks while accelerating decarbonization. One typical implementation of ML is DeepMind’s collaboration with Google to forecast wind power 36 h in advance using artificial neural networks. This initiative has increased the economic value of wind power by making it more predictable and reliable for grid integration. Another famous case is the use of ML-based intelligent dispatch systems by the National Grid of China to balance electricity supply and demand in real time. These systems optimize load distribution and integrate variable renewable energy sources such as solar and wind, minimizing reliance on peaking fossil fuel plants. Such use cases demonstrate the technical feasibility of ML to improve energy forecasting, grid stability, and operational efficiency [105,106,107]. ML is excellent at handling large, complex data sets of weather, demand, and energy production, enabling dynamic adaptation to changing climate conditions. For example, ML can adjust dispatch strategies based on real-time weather events or extreme climate anomalies, increasing grid resilience. By predicting renewable energy generation patterns and consumer demand, ML supports better management of energy storage and reduces clean energy curtailment. These examples illustrate how ML is already operational in critical infrastructure, paving the way for scalable, next-generation climate adaptation [108,109,110].

4.2. Economic Consequences of Achievement

The economic implications of using ML to adapt to climate change through improved integration of renewable energy sources are significant [111]. ML improves the efficiency and reliability of renewable energy systems, reducing operating costs and increasing return on investment [112]. Improved grid forecasting and optimization reduce energy waste and lower electricity prices for consumers. Utilities and energy companies can better manage supply and demand, reducing the need for costly infrastructure expansion or fossil fuel backups. Improved efficiency extends the life of renewable assets, delaying replacement costs and increasing long-term profitability [113]. Investments in ML-based energy solutions drive innovation and create high-tech jobs in data science, engineering, and energy management. Governments and businesses benefit from better-informed assessments of climate risk, allowing for smarter allocation of adaptation resources [114]. ML supports decentralized energy models, such as microgrids, encouraging economic development in remote or underserved areas [115]. Reduced energy variability and dependence on fossil fuels increase national energy security and economic stability. Thus, ML integration accelerates the transition to a profitable, resilient, and sustainable low-carbon economy [116].

4.3. Societal Consequences of Achievement

The societal implications of using ML to adapt to climate change through the increased integration of renewable energy sources are far-reaching [117]. Communities benefit from more reliable and affordable clean energy, improving quality of life and reducing energy poverty. ML-based systems enable the early warning of extreme weather events, increasing public safety and disaster preparedness. As energy systems become more efficient and sustainable, public health improves through reduced pollution from fossil fuels [118]. Education systems evolve to include digital and energy literacy, preparing future generations for a green, technology-driven economy. ML empowers citizens and local governments with data to better make decisions about resource use and plan for climate resilience [119]. Jobs created in the renewable energy and AI sectors offer new career paths, especially for youth and underserved populations [120]. Rural and remote communities gain better access to energy through smart microgrids and decentralized solutions. Greater trust in technology and data-driven governance can strengthen civic engagement and environmental responsibility. Thus, ML integration supports a more just, climate-conscious, and resilient society [121].

4.4. Limitations

One of the main limitations is the reliance of machine ML on large, high-quality datasets that are often sparse, inconsistent, or proprietary in the energy and climate sectors. ML models can struggle to cope with the high variability and uncertainty of renewable energy sources such as solar and wind, leading to inaccurate predictions [122]. Most ML algorithms are black-box models, making their decision-making processes difficult to interpret, which is problematic for critical infrastructure such as power grids. There is a lack of standardized benchmarks and criteria for evaluating ML performance in renewable energy applications, complicating validation and comparison [123]. ML solutions often require significant computational resources that may not be sustainable or available in developing or resource-constrained regions. Many models are over-fit to specific regions or datasets, reducing their effectiveness in broader, real-world climate adaptation scenarios [124]. Integrating ML into existing energy systems is technically complex and requires interoperability with legacy infrastructure that is often outdated [125]. ML cannot always account for extreme or rare climate events, limiting its usefulness in predicting or preparing for worst-case scenarios. Implementing ML in real time in energy systems requires very low latency and robust cybersecurity, which is a technical challenge [90]. Ethical and privacy concerns related to the collection and use of energy and climate data can limit access to data and model development. ML applications are often not tailored to local socioeconomic contexts, reducing their relevance or effectiveness in some regions [126]. The short life cycles of ML tools and platforms can result in outdated models and require constant updates and retraining, which is resource-intensive. A shortage of interdisciplinary talent with expertise in both ML and renewable energy systems hinders innovation and implementation. ML models can inadvertently amplify existing inequalities if trained on biased or incomplete data, leading to an unequal distribution of adaptation benefits. The lack of long-term policy and regulatory support for ML-based renewable energy integration hinders the scalability and sustainable impact of climate adaptation efforts [127].

4.5. Directions of Further Research

Future research should focus on developing robust, transparent, and explainable ML models to increase trust and accountability in critical energy systems. Research should aim to increase data availability and quality, including the creation of open, standardized data sets for renewable energy and climate variables. There is a need for research on hybrid models that combine physical simulations with ML, improving accuracy in dynamic and uncertain renewable energy environments [128]. Further research on ML for energy forecasting at multiple scales—from local solar production to national demand patterns—can significantly improve grid reliability. Research should investigate how ML can be integrated with energy storage and demand management to balance supply and improve efficiency [129]. Emphasis should be placed on low-resource ML techniques that require less data and computing power, enabling wider adoption in developing regions. Future research should explore equity-aware ML frameworks that ensure the equitable distribution of renewable energy benefits across communities [130]. There is also a need to explore real-time ML implementation architectures, such as edge computing, for decentralized and resilient energy systems. Researchers should assess the long-term environmental and economic impacts of integrating ML-driven renewable energy sources in climate change adaptation scenarios [131,132,133,134,135]. Interdisciplinary research spanning climate science, social science, policy, and computer science is essential for designing ML solutions that are scalable, inclusive, and climate-resilient [136,137,138,139,140,141].

5. Conclusions

ML offers transformative potential to increase renewable integration and overall system efficiency, making it a key tool in climate change adaptation strategies. The greater the changes, the greater the effort required to provide favorable living conditions; hence, research and operational efforts have already begun but must also gather pace. Weather conditions are a regional factor, strongly varying with geography, time of day, and seasonal sequences. By enabling the accurate forecasting of energy generation and demand, ML helps balance supply with consumption, mitigating the intermittency of renewable energy sources such as solar and wind. Intelligent grid management powered by ML enables real-time decision-making, reducing energy waste and improving reliability. In addition, ML-based optimization improves maintenance schedules and resource allocation, reducing operational costs and environmental impact. Our review confirms that a broader adoption of ML can accelerate the transition to a low-carbon energy system by improving the efficiency and scalability of renewable energy sources. However, successful implementation requires not only ensuring technological capabilities (robust infrastructure, structured data sets, and interdisciplinary collaboration) but also the careful consideration and alignment of ethical and regulatory factors from strategic to regional and local levels. Integrating ML into energy systems, underpinned by appropriate regulations, is a key step towards resilient, efficient, and sustainable climate change adaptation efforts.