Hydrodynamic models are essential for understanding flood processes, but their computational demands make them unsuitable for updating flood risk in dynamic storm conditions. This study presents an alternative approach that combines crowdsourced information with a Bayesian Neural Network (BNN) to generate full-field, near–real-time flood risk maps with modest computational requirements. This framework, applied to Pinellas County, Florida, and tested across two storms, leverages community-sourced information to update model predictions. Another key contribution is the use of full predictive distributions rather than single deterministic estimates. While crowdsourced information provided voluntarily can validate strong model performance where observations are available, such information may be inherently incomplete and spatially biased. The predictive distributions reveal a broad spectrum of flooding outcomes and highlight areas where averaged estimates, although operationally useful for decision-making, may mask high-flood-risk scenarios. This range of uncertainty provides transparent guidance for targeted verification thereby mitigating the inherent bias of voluntary reporting.