Flooding is one of the most frequent and costliest hydro-meteorological hazards that affects every country worldwide and contributes to significant societal and financial losses. While there is no shortage of Earth Observation (EO) datasets and hydrodynamic models to map, forecast and monitor flood events, decisionmakers and first responders face significant challenges in using these products. To assist with resource planning, emergency management and downstream analytics, we have developed a data-driven machine learning model-Flood Risk Index for Resilience (FRI-R). The index is built on Model of Models (MoM),an operational open-source ensemble model, that integrates outputs from hydrologic models and EO data (optical imagery) and forecasts flood risk globally at sub-watersheds every 24 hours. Based on historical MoM outputs, the index accounts for spatial and temporal distribution of flood events and identifies high to low-risk watersheds based on the duration and frequency of past flood events. Considering the global focus to improve community resilience to extreme weather events, MoM has been successful in disseminating early flood warnings based on forecasted risk. FRI-R expands MoM's usability by identifying high flood risk watersheds which could be used to geotarget priority populations and critical infrastructures to mitigate flood impacts and improve resilience.