Climate change presents one of the most critical challenges of the 21st century, with its adverse impacts being observed across global ecosystems. Accurate prediction of environmental patterns is essential for proactive climate adaptation and mitigation strategies. In this research, we investigate the integration of artificial intelligence, specifically machine learning (ML), with remote sensing technologies to enhance predictive accuracy in climate-related studies. Satellite imagery, sourced from platforms such as NASA's MODIS and ESA's Sentinel missions, forms the primary dataset for analysis. Through rigorous preprocessing and feature extraction techniques, environmental indicators such as vegetation indices, land surface temperature, and moisture levels are derived. Several ML models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and ensemble methods like Random Forest and XGBoost, are developed and evaluated for their capability to detect and forecast spatial-temporal environmental trends. Experimental results demonstrate that deep learning models outperform traditional algorithms in capturing complex patterns and regional variations. Notably, the LSTM-CNN hybrid model exhibited superior performance in forecasting multi-temporal changes in vegetation density and surface heat signatures. The findings highlight the potential of AI-driven models to contribute substantially to climate change monitoring and decision-making frameworks. This study underscores the relevance of combining geospatial intelligence with data-driven learning approaches, paving the way for more resilient and informed environmental policy interventions.

Keywords: Climate Informatics, Machine Learning, Satellite Remote Sensing, Environmental Forecasting, Deep Learning Models, Spatio-temporal Analysis