Climate change involves complex interactions of atmospheric conditions, ocean currents, human activities, and environmental factors that challenge traditional modelling approaches. With vast, expanding datasets from satellites, sensors, and weather stations, conventional methods struggle with real-time analysis and accurate predictions. Artificial Intelligence has emerged as a transformative data-driven technology for forecasting climate scenarios through enhanced analysis of dynamic climate data. However, current AI-CC interconnections remain unclear, with fragmented knowledge hindering climate adaptation strategies. This study systematically evaluates AI and Climate Change Prediction through bibliometric analysis of Scopus literature following PRISMA criteria. Using Vos Viewer, Biblioshiny, and R Statistical Software, the research analyses publication trends, sources, and countries across 1990–2024 (historical trends) and 2020–2024 (next-generation technologies). Results demonstrate exponential publication growth (R2 = 0.8205), particularly post-pandemic. While the USA dominated until 2015, China has emerged as a major research hub. Thematic mapping identifies climate modelling and predictive analytics as pivotal for environmental resilience. Critically, overall CC-AI integration remains limited, marked by disciplinary disconnects. Future priorities include advancing scenario prediction, adaptive techniques, predictive modelling accuracy, and clarifying data-driven AI-CCP relationships to maximize next-generation technologies' potential in climate adaptation.