Floods are one of the most dangerous natural disasters, both frequent and dynamic due to continuous land use changes and climate change. This causes difficulty in predicting the areas most vulnerable due to their complex nature, causing heavy loss and damage. The study presents a data-driven framework for flood susceptibility mapping, examining the influence of multiple satellite-derived geo-spatial and temporal features in the Cachar district of Assam, India—a region frequently impacted by monsoonal flooding. By integrating Machine Learning with features derived from NDVI (Landsat 8), LULC (Sentinel-2), topographic variables (SRTM DEM), soil texture (OpenLandMap), and monsoon precipitation (CHIRPS), alongside flood extent information obtained from NDWI and Sentinel-1 SAR data, the model aims to enhance predictive accuracy in flood-prone, data-constrained environments. A rigorous feature selection process using IGR and VIF score and comparative evaluation across various classifiers was used to optimize the model. The study highlights the importance of integrating machine learning with remote sensing data to construct a precise flood risk model to aid the disaster management team in identifying vulnerable regions.