<p>Climate change has intensified the need for more effective analytical tools to support mitigation, adaptation, and long-term sustainability planning. In this context, this study aims to systematically map the intellectual structure, application domains, and governance implications of research on artificial intelligence (AI) in climate governance and sustainability pathways through a combined bibliometric and thematic review. The analysis covers 116 Scopus-indexed publications published between 2010 and 2026. Quantitatively, the corpus is dominated by research articles (88.8%), non-generative AI applications (90.5%), and decision-support or policy-analytics studies (63.8%). Publication output increased sharply after 2019, with approximately 40% of the corpus published in 2025. Thematic analysis identifies three major domains: governance and policy analytics (56.0%), adaptation and resilience (32.8%), and mitigation and energy optimization (11.2%). The findings show that AI is used primarily as a decision-support resource rather than an autonomous governance mechanism. At the same time, the literature highlights persistent challenges related to transparency, accountability, unequal regional data infrastructures, and the environmental cost of computation. Overall, the study contributes to the emerging field of algorithmic climate governance by showing that effective AI use in climate policy depends on transparent, context-sensitive, and institutionally grounded governance frameworks.</p>