Coastal communities in Texas and Louisiana face accelerating compound flood risks driven by sea-level rise, intensifying rainfall, subsidence, and rapid urbanization. While physics-based hydrodynamic models provide essential mechanistic understanding, their computational cost limits real-time forecasting, ensemble uncertainty analysis, and community-scale scenario testing. To address this gap, we developed a hybrid AI–numerical modeling framework that fuses high-resolution COAWST/ROMS simulations with advanced machine-learning architectures following the P2M (Prediction-to-Map) methodology. Applications across the Louisiana and Texas coasts demonstrate that this approach preserves the fidelity of full hydrodynamic models while delivering four to five orders of magnitude gains in computational efficiency, enabling near-instantaneous flood prediction, probabilistic hazard mapping, and rapid evaluation of thousands of storm, rainfall, and land-use scenarios. The AI surrogates remain tightly anchored to physical model outputs, ensuring interpretability, robustness, and transferability across coastal settings. Building on this foundation, we present a vision for a next-generation AI-enhanced flood mitigation engine—a scalable digital-twin platform integrating physics-based modeling, real-time data assimilation, AI ensemble forecasting, and critical-infrastructure vulnerability analytics to support emergency managers and planners with rapid scenario exploration and optimized mitigation strategies. This framework aligns directly with the Community Co-Financed Flood & Energy Resilience (CCOFFER) initiative, which leverages digital-twin capabilities, AI-based forecasting, and land-value-capture mechanisms to co-design equitable, financeable resilience pathways for Gulf Coast communities. Together, the hybrid AI approach and the CCOFFER vision outline a transformative path from prediction to proactive, community-driven flood mitigation.