Urban flooding can disrupt homes, transport,essential services, creating a need for timely and reliable mapping. This study develops a machine learning approach for mapping flood inundation in Mumbai using satellite observations, elevation, rainfall, and river-related data. Results show that combining these sources improves flood classification and helps identify areas with greater flood probability. Approach supports informed flood risk assessment and future decision-support applications.