Surface water resources play a pivotal role in a multitude of human activities, ranging from providing water for cities, supporting fishing and navigation, facilitating irrigation, to generating hydropower. The hydrological patterns of river systems have profound consequences on environmental sustainability, water-related hazards, human well-being, and a wide array of ecosystem services. With the anticipation of global warming affecting surface water availability, quality, and distribution, it has become imperative to analyze water utilization efficiency and the prevalence of water-related risks. Hence, it is crucial for communities to devise strategies and adapt to the potential consequences of climate change. This study presents an innovative approach to mapping floods in the Oubangui River section of the Congo Basin. It combines Sentinel-1 Synthetic Aperture Radar (SAR) imagery with a calibrated and validated Soil and Water Assessment Tool (SWAT) model. The methodology involves extracting surface water data from SAR images and establishing a relationship between pixels and water discharge values. This information enables the creation of a spatial pixel probability distribution, facilitating accurate mapping of flooded and dry areas. The results underscore the efficiency and accuracy of this approach, with an 85.9% accuracy in predicting water pixels and a 97.9% accuracy in predicting land pixels. The approach significantly enhances the region's ability to respond effectively to flood events, offering valuable insights for flood mapping and disaster management in this ecologically important and vulnerable area.