Rapid urbanization and the loss of vegetative cover in Indian cities have raised serious concerns about environmental sustainability and public health. This study focuses on analysing and forecasting Urban Heat Island (UHI) patterns in <em>Jaipur, India</em>, by examining both Surface UHI (SUHI) and Atmospheric UHI (AUHI). Using Google Earth Engine, the research integrates diverse spatio-temporal datasets—including Landsat-derived indices (such as LULC, NDVI, NDWI, NDBI, NDMI, albedo, and emissivity), geospatial features (building density, sky view factor, and population density), and meteorological data (air temperature, humidity, wind speed, and solar radiation) from 2000 to 2024—to train a Random Forest Regression model. The model demonstrated strong performance (R² = 0.806; RMSE = 0.059), surpassing linear and generalized additive models by effectively capturing complex, non-linear relationships. It also helped identify high-risk areas like Transport Nagar and Budhsinghpura. Projections for 2030 and 2035 indicate increasing heat stress, particularly in<em> Jaipur’s</em> expanding urban periphery. This GIS-integrated machine learning framework presents a replicable approach for UHI prediction in other fast-growing Indian cities.