Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review

The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.