Floods are the most damaging natural disasters, affecting millions of people every year. In India, flood forecasting is often limited by the lack of data and tools that can predict risks at large river basin scales while also considering the local vulnerabilities of affected communities. This study presents a new flood risk forecasting framework designed to help disaster managers prioritize emergency response efforts more effectively. The framework combines advanced rainfall forecasts from the Global Ensemble Forecast System (GEFS) with a hydrodynamic flood simulation model (MIKE 21) to create high-resolution flood maps. A unique metric called “Critical Response Time” (CRT) is introduced to show how quickly different areas may get inundated, helping authorities plan timely interventions. Additionally, socioeconomic vulnerability (SEV) data—such as population density and access to resources—are layered on top of the hazard maps to highlight which areas are most at risk. We apply this method to the Krishna River Basin (KRB), a large and flood-prone region in India. The results provide detailed maps showing zones where flood hazards and community vulnerabilities overlap. This approach is adaptable to other regions and aims to improve early warnings and disaster response strategies, ultimately reducing the impact of floods on vulnerable populations.