Abstract
Climate change poses one of the most significant challenges to global sustainability. This study presents a comprehensive machine learning framework for predictive climate modeling, integrating multiple data sources including satellite imagery, ocean temperature readings, and atmospheric composition measurements. Our novel ensemble approach combines convolutional neural networks with recurrent architectures to capture both spatial and temporal patterns in climate data. Results demonstrate a 23% improvement in prediction accuracy compared to traditional statistical models, with particular success in forecasting extreme weather events. The model was validated across five geographical regions, showing robust performance in diverse climate zones. This research contributes to the growing body of work leveraging artificial intelligence for environmental science, offering new tools for policymakers and researchers to better understand and respond to climate dynamics.