Artificial Intelligence (AI) is increasingly recognized as a transformative tool in addressing the dual imperatives of climate change mitigation and adaptation. This review provides a comprehensive synthesis of the current state of AI applications that contribute to reducing greenhouse gas emissions and enhancing resilience to climate-related hazards. It systematically examines advances in machine learning, optimization, and data-driven decision support across key domains including renewable energy forecasting, energy system optimization, land-use planning, disaster risk management, precision agriculture, and water resource allocation. The paper also analyzes the enabling infrastructure required for scalable and ethical deployment, such as data interoperability, model interpretability, and integration with physical system models. The findings from existing literature indicate that AI has significantly improved predictive capacity, operational efficiency, and adaptive planning in climate-related contexts. However, persistent challenges ranging from data scarcity and geographic bias to the carbon footprint of AI systems and governance limitations continue to constrain equitable implementation. The review concludes by identifying critical research gaps and proposing a strategic roadmap focused on interdisciplinary collaboration, equitable data frameworks, and policy alignment with global climate objectives. By critically appraising both the potential and limitations of AI, this review contributes to the research on how intelligent systems can be leveraged to support sustainable, inclusive, and scientifically grounded climate action.
Eze et al. (2025) studied this question.
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