Urban heat islands (UHIs) present a critical challenge to sustainable urban development, demanding high-resolution monitoring tools for effective climate adaptation. We address this need by implementing a machine learning framework for downscaling Land Surface Temperature (LST) data, demonstrating its ability to capture fine-scale thermal variations. The methodology leverages multi-sensor remote sensing data fusion, integrating high-resolution optical observations from Sentinel-2 with thermal imagery from Landsat 9 (daytime LST reference) and ASTER (nighttime LST reference). Random Forest (RF) regression is employed, utilizing Sentinel-2 multispectral bands, derived spectral indices (e.g., NDVI, NDBI) to characterize land cover, and a Digital Elevation Model (DEM) to account for topographic effects. The RF model was rigorously trained, and its hyperparameters optimized via randomized cross-validation to predict LST at a 10-meter resolution. Results demonstrate robust performance, achieving a high R² of 0.75 (Mean Absolute Error, MAE: 1.7°C) for daytime LST and R² of 0.50 (MAE: 0.6°C) for nighttime LST. The resulting downscaled maps delineate pronounced heat accumulation in dense built-up areas, notably its historic center and large commercial zones, contrasting sharply with cooler vegetated areas and green urban corridors. A comparative assessment against bilinear interpolation, TsHARP thermal sharpening, and linear regression confirms that the RF framework achieves the best balance between predictive accuracy, spatial coherence with the source thermal data, and meaningful sub-pixel detail, effectively preserving the critical fine-scale thermal patterns. Ultimately, this study advances UHI monitoring by enabling the precise identification of heat-vulnerable areas, thereby supporting targeted mitigation strategies even in small and medium-sized cities.