Artificial Intelligence for Climate Action: A Data-Driven Methodological Framework for Sustainable Climate Analytics

Owing to the rapid and accurate processing of intricate environmental data, artificial intelligence has become one of the key players in climate change (in combating them). This will provide unparalleled forecasting power and enables decision support systems to address both avoidance of climate impacts as well as adaption. This review provides a systematic overview of recent advances using AI across climate science from machine learning, deep learning, optimization methods and hybrid modeling frameworks. We summarize advances in key areas including prediction of extreme weather events, tracking emissions, renewable energy systemoptimization, disaster risk assessmentand climate adaptation planning. Literature supporting AI has shown that it provides tangible improvements in predictive accuracy and operational performance, across these climate-related use cases. However, significant challenges remain, including data availability — both structured and unstructured, bias in algorithmic models, limited transparency of black-box systems and the high energy requirements from computationally expensive models. Using synthesized evidence from over 200 original studies and major review articles, this article identifies research knowledge gaps and outlines a pathway for responsible, scalable and equitable AI deployment in support of Sustainable Development Goal 13 (Climate Action) and wider sustainability initiatives.