Floods are a major cause of mortality, facility destruction, and serious economic impact on a country. It is a catastrophe, it would be difficult; the government, relevant groups, and the community must implement precautions to mitigate their destructive impacts. Emergency management organizations must execute actions before a flooding catastrophe to reduce risks and prepare an emergency reaction during this kind. To do this, the most cutting-edge technologies must be used to foresee calamities as fast as possible so that suitable reaction strategies may be developed before the calamity. Flooding is unpredictable and depends on meteorological and ecological variables, making it challenging to forecast. This classification system divides three types of remote sensing technologies—multispectral, radar, and coupled with GIS for integrating hydrological and hydraulic models to predict floods. Observing the high NDVI results ensures healthier vegetation, whereas the stressed vegetation carries lower NDVI values. Then, an approach for forecasting floods and a map has been proposed to fill the existing gaps. The results illustrate the NDVI in flood prediction.