Flood hazard management in small tropical river basins faces significant challenges owing to rapid hydrological responses and a lack of hydrometric instrumentation. This study aimed to bridge the technological gap in environmental monitoring by developing a high-precision flood hazard model capable of operating in data-scarce regions where traditional hydrodynamic models fail because of insufficient parameterization. The methodology integrates Sentinel-1 SAR imagery (2019–2025) and the Random Forest (RF) machine learning algorithm within the Python platform. This study reconstructed historical flood dynamics and predicted spatial hazard zones using ten environmental parameters. The results demonstrated robust model performance with a validation accuracy of 94.45%, an Area Under Curve (AUC) of 0.98, and a sensitivity of 96.80%, significantly outperforming conventional statistical methods, which typically achieve lower accuracy in flashy watersheds. Spatially, the model identified 1,080.24 hectares (11.59% of the total area) as Very High hazard zones concentrated in the downstream alluvial plains. Furthermore, Explainable AI (SHAP) analysis revealed that vegetation density (NDVI) and topography are the primary physical determinants of inundation, surpassing the influence of local rainfall variability. These findings provide a scientifically validated framework for precise hazard zoning, confirming that machine learning integration can effectively substitute dense ground gauge networks to develop resilient environmental protection strategies.