The escalating threat of climate-driven flooding creates an urgent imperative for disaster preparedness systems that are both rapid and precise. Traditional hydrodynamic models, such as HEC-RAS, provide the necessary physical fidelity for mapping inundation but are computationally prohibitive for real-time Early Warning Systems (EWS). Conversely, standard library interpolation methods often fail to capture the non-linear complexities of modern flood dynamics, particularly when accounting for multiple hydrological drivers. This Master's thesis addresses this critical operational gap by developing and rigorously evaluating Machine Learning (ML) surrogate models capable of predicting flood extents instantly without sacrificing hydraulic accuracy. The study focuses on a high-risk 98 km reach of the Po River (Cremona-Borgoforte), utilizing high-fidelity HEC-RAS simulations to generate a synthetic training dataset.