The proliferation of satellite imagery and environmentalmonitoring systems has generated unprecedented volumesof geospatial data, necessitating advanced computational methodsfor effective analysis and interpretation. This comprehensivereview examines recent developments in machine learning techniquesapplied to satellite image analysis, with particular emphasison three critical domains: deep learning approaches for clouddetection and segmentation, spatial clustering methodologies forgeospatial data analysis, and time series forecasting models forenvironmental prediction. Through systematic analysis of twelverecent research contributions, this paper identifies key technologicaladvances, methodological innovations, and emergingtrends in each domain. Deep learning segmentation approaches,particularly U-Net variants enhanced with attention mechanismsand ensemble methods, demonstrate superior performance incloud detection tasks with accuracy rates exceeding 95%. Spatialclustering techniques incorporating DBSCAN algorithms andhierarchical mixture models show significant improvements inurban delineation and environmental pattern recognition. Timeseries forecasting models, especially transformer-based architecturesand fuzzy-enhanced LSTM networks, achieve remarkableaccuracy in long-term environmental prediction with reducedcomputational overhead. The integration of these methodologiespresents substantial opportunities for advancing automated environmentalmonitoring, climate research, and disaster managementsystems.