Despite the growing body of literature on AI and its applications to the SDGs, studies that deeply integrate AI methods with SDG-related research remain surprisingly sparse. Most research focuses either on the technical aspects of AI or on addressing specific SDGs. The intersection where AI tools are applied to solve complex sustainability challenges or contribute to SDG attainment has hardly been realized to date.
Focus on local studies and gaps in social sustainability
There has been a substantial increase in the number of annual publications on sustainable development research using AI, particularly since 2019. This surge is consistent with broader trends in sustainability and AI research, as evidenced by some 600,000 articles published on sustainability (article includes ‘sustainab*’) and 400,000 articles published on ‘artificial intelligence’ in the first 10 months of 2024 alone (Scopus query 3 November 2024). A literature review covering the period 1990 to 2014 first documented the rapid rise of AI-related research, driven in part by third-party funding and increasing global interest20. This trend underscores the high relevance of both sustainability and AI as research domains.
While AI research output is positively correlated with third-party funding availability in earlier research20, this pattern was only partly confirmed in our study. For example, the prominence of AI-based watershed modelling in Spain, India and Iran reflects well-established research traditions21,22. In Spain, highly cited publications on AI by Spanish scholars have been documented since the 1990s20. Similarly, Italy’s notable focus on AI-based research on SDG 3 (good health) is related to several (national) initiatives that encourage data sharing and collaboration23,24. China’s AI strategy, embedded in its New Generation of Artificial Intelligence Development Plan until 2030, prioritizes economic development via megaprojects25. However, our review finds that most Chinese publications focus on climate change, clean energy and education, which clearly suggests a deviation from purely economic goals.
Despite this progress, the United Nations‘ 2023 and 2024 Sustainable Development Goals Reports highlight the lack of progress on Agenda 2030, with half of SDG targets off-track and insufficient data for most goals in 202326,27. The United Nations has advocated that AI support the SDGs26, but gaps remain. For example, AI is widely used in health, education and environmental modelling, but its use in poverty reduction (SDG 1) is minimal. We found only seven reviews and no empirical studies in the most cited literature applying AI to SDG 1 (no poverty). This finding is striking given that 575 million are projected to live in extreme poverty in 203026. Research on poverty relies predominantly on qualitative approaches or analyses of demographic data, for example, ref. 28, with few examples of AI-driven approaches for poverty prediction tools29.
The proliferation of machine-learning, deep-learning and evolutionary algorithms over the past decade has had a profound impact on environmental sustainability research. These methods excel at processing large-scale, sensor-based and image-rich datasets, making them particularly effective for tasks such as vegetation monitoring, water resource management and clean energy optimization. For example, they are used to track vegetation changes through satellite imagery, predict water levels and optimize renewable energy grid performance30. Evolutionary algorithms in particular have proved useful in complex optimization problems, such as wind farm layouts and solar panel configurations, while balancing environmental and economic objectives31.
In contrast to its environmental applications, AI remains underutilized in areas of social sustainability, such as policymaking, education for sustainable development and social equity, which are critical to the SDGs. This disparity, apart from the historical overrepresentation of some research fields32, is due to several challenges:
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Complexity of social data: social systems involve qualitative and contextual variables that are difficult to model qualitatively.
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Ethical constraints: privacy laws and ethical concerns limit access to human behavioural data.
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Supervised learning limitations: many AI methods require large, labelled datasets that are sparse and context-dependent in political and social domains33.
These limitations contribute to notable gaps in AI applications for critical dimensions of sustainable development, such as SDG 10 (reduced inequalities) and SDG 16 (peace, justice, and strong institutions). Addressing these gaps requires more inclusive data frameworks that account for qualitative and context-rich variables, greater interdisciplinary collaboration to integrate AI with social sciences, and algorithmic innovations tailored to the complexity of social systems5. Our analysis reveals a gradual increase in the body of literature on AI in sustainable development research. However, many AI users tend to adopt techno-optimistic or ecomodernist perspectives, or align with other viewpoints that position technology as a great leap towards solutions. Correspondingly, working on social sustainability solutions with AI may require a deeper mindset shift3.
Disciplinary AI techniques by few research communities
The regional scope of most of the empirical articles in this study reflects the thematic focus and methodological nature of the research. For example, many studies deal with regional watershed assessments, for example, refs. 34,35. Other region-specific applications range from evaluating AI tools in education, such as single learning platforms, for example, ref. 36, to optimizing energy consumption in greenhouses37. By contrast, the few global empirical studies that we found primarily address system optimization, data mining and remote sensing, for example, refs. 38,39. However, the limited number of global studies may be due to the complexity and the high computational demands of AI methods, which remain a substantial barrier to large-scale applications40.
Although sustainable development inherently requires transformative, longitudinal research (Fig. 2), most empirical studies focus on the present and use quantitative approaches. This is consistent with previous observations in health-care research, where AI-powered studies rarely address temporal dynamics41. Furthermore, SDG terminology often functions as a rhetorical device rather than as the basis for actionable insights or transformative change42. Our findings suggest that the connection between AI methods and sustainable development research remains nascent, characterized by experimental applications and buzzword use rather than substantive contributions to sustainability goals.
The current research landscape is highly fragmented, with a clear disciplinary bias towards forecasting and optimization in technical areas such as water resource management, vegetation monitoring, energy systems and pollution control. This reflects the ongoing experimental phase of AI development, as researchers continue to explore its potential to advance the SDGs5. However, emerging breakthroughs in natural-language processing applications such as ChatGPT and other generative AI technologies are expected to shift research priorities. In the coming years, AI applications are likely to expand into the social sciences, psychology and education, enabling more nuanced investigations of societal changes43,44.
Disciplinary focus within the SDG perspectives
The disciplinary divide in sustainable development research, as illustrated in Fig. 2, is a well-documented phenomenon. While a growing body of transdisciplinary literature challenges this divide, particularly in the context of AI45, two distinct patterns emerge in our analysis. First, there is a clear distinction between studies that focus on the application of AI and those that use AI as a methodological approach to advance knowledge on sustainable development. For example, prediction is a common technique in both smart agriculture and clean energy research, but the former emphasizes remote sensing for image detection, while the latter focuses on grid and physical systems optimization46,47.
Most SDGs are directly represented in the groups that we find, including SDG 3 (health), SDG 4 (education), SDG 6 (water), SDG 7 (clean energy) and SDG 15 (life on land). Despite the extensive use of AI for many years in these areas, the conceptualization of sustainability in these studies remains weak (Supplementary Fig. 4). This narrow framing reflects the disciplinary silos from which causal reasoning is derived, often at odds with the solution-oriented, systems-level agenda in sustainability science48.
Achieving SDG 17 (partnership for the goals) will require inter- and transdisciplinary research pathways across all SDGs, potentially catalysed by the diverse applications of AI explored in this study. Examples include AI’s role in fostering collaboration, supporting data integration, and bridging disciplinary boundaries to address complex sustainability challenges.
However, notable gaps remain. AI-based research on poverty (SDG 1) and gender (SDG 5) is underrepresented (Supplementary Fig. 1). For poverty, we found only seven review articles, while for gender there were eight articles (five empirical and three conceptual or review). Despite its foundational importance27, poverty is often framed in terms of economic welfare49 or as an implicit component of broader sustainable progress metrics such as GDP or inclusive welfare50. In particular, smart agriculture is presented as a ‘pathway out of poverty’ in rural contexts51.
Gender (SDG 5) research has focused primarily on bias in research itself, such as, for example, in a study on gender representation in Canadian AI research52. In health care, AI algorithms often do not account for sex and gender bias53, limiting their equity and effectiveness. Addressing such disparities is critical to advancing social equity and inclusivity in AI applications across sustainable development domains. Certain SDGs, such as those related to industry and consumption, have long been closely associated with AI and data-driven analytics, even before the formal introduction of the SDGs. As a result, these SDGs are covered by a more extensive body of scientific literature. By contrast, other SDGs, such as poverty and gender equality, do not have a similarly well-established tradition of AI-driven quantitative data analysis. While relevant data sources exist for areas such as gender research, addressing this research gap remains a future challenge for the communities working on these specific SDGs.
Limitations of our study
Our analysis has several limitations, which stem from the scope and methodology of our review. First, our selection of articles was constrained by search terms that aimed at the intersection between AI and the SDGs. Since the SDGs represent a political framework and a policy compromise, many articles that explore the broader intersection between sustainability and AI may not have been included in our dataset. Second, our focus on the most cited articles introduces a potential bias towards well-established research, which may miss emerging studies with lower citation counts (Supplementary Information I and Supplementary Tables 4 and 5). Third, both the SDG as well as the AI communities often publish findings through policy reports or conference proceedings, which creates delays associated with the duration of the peer review process. This leads to a lag in the visibility of emerging patterns in the journal-based literature. Despite these limitations to our analysis, we believe that the patterns identified in our review are robust and unlikely to change significantly with the inclusion of more previously published literature or grey literature. However, future developments in the field may naturally refine or extend the findings presented here.
Our work provides a solid foundation to enable a more substantiated discussion about the current state of the art of the intersection between AI-focused and SDG-focused scientific literature. We present the patterns in the literature published to date to contribute to a change in the future literature. We highlight gaps and a lack of deeper ties between the two emerging research fields. The main aim of our review is thus to present an overview of the current literature, ideally to stimulate the discussion on AI and sustainability and to highlight where new lines of thinking need to emerge. It is clearly beyond this review to provide a more in-depth discussion on future trajectories. However, we hope to contribute by engaging in a critical debate on a larger integration.
Limitations of the current literature and outlook
The literature reviewed broadly reflects the three pillars of sustainability: social, environmental and economic54. However, notable gaps remain in the application of AI to specific SDGs. Specifically, SDG 1 (no poverty), SDG 5 (gender equality) and SDG 17 (partnership for the goals) remain severely underrepresented. These goals are foundational to the SDG framework, highlighting the interdependence between economic progress and societal well-being55. Their relative neglect in AI and sustainability research exacerbates climate injustice and undermines the capacity for long-term and inclusive collaboration across disciplines and between science and society.
The uneven distribution of articles across SDGs is partly due to the different definitions and applications of AI in the current literature. Many studies adopt a broad or undefined understanding of sustainability, often using the term without providing a clear definition. This lack of conceptual clarity reflects a broader problem: most studies are limited to systems knowledge, failing to address the normative or transformative dimensions that are central to sustainability science. Ethical considerations related to AI are clearly important for both research and policy, but they are almost entirely absent from the research reviewed. Beyond this review, they remain highly fragmented and insufficiently integrated. Prominent ethical debates typically revolve around the optimization of energy use, so-called ‘Green AI’, the growing energy demand due to AI, the efficiency of production and consumption processes, and the accessibility and confidentiality of training and test data56,57,58. However, there are deeper and often more tacit debates about national AI systems, their use, and the competitive dynamics between countries. Moreover, broader concerns about the potential risks of AI—such as its ability to control and disempower citizens, or even pose existential threats to humanity—are also central to these debates59,60,61. Although the data sources examined in this review underscore the importance of these issues, a comprehensive and substantial body of literature addressing them remains largely absent from the available literature. This omission contrasts sharply with the normative and values-driven nature of sustainability, which seeks to address profound societal challenges through inclusive and ethical solutions.
In addition, AI itself poses potential risks to sustainability, such as its high energy consumption and other environmental impacts, which can exacerbate challenges such as climate change. This highlights the responsibility of researchers to ensure that AI applications are consistent with sustainability principles and make a positive contribution to our common future.
While research on the intersection between AI and sustainability has grown exponentially in recent years, many SDGs remain underexplored. Current research often represents an innovative but opportunistic starting point, with limited integration across disciplines or transformative contributions to sustainability goals. To address these gaps, a more integrated research agenda is needed—one that emphasizes interdisciplinary collaboration, ethical considerations and the normative aspects of sustainability. Such an agenda has the potential to advance the role of AI as a transformative tool for achieving sustainable development in the years to come.
