Floods continue to be one of the most severe natural hazards, with their frequency and intensity rising due to climate change and rapid urbanization. Traditional flood forecasting approaches, based on statistical models such as Gumbel, Log-Normal, and Log-Pearson Type III distributions, have provided useful estimates but are often constrained in handling nonlinear hydrological processes and uncertainties. To overcome the existing limitations in flood prediction and management, the present research develops an AI-driven hydrodynamic framework tailored for the Panam River Basin, a flood-prone tributary of the Mahi River located in western India. The proposed approach combines data-driven intelligence with process-based modeling by integrating Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic with HEC-RAS hydrodynamic simulations. This hybrid setup enables accurate flood forecasting, generation of inundation maps, and systematic prioritization of flood risk zones across the basin. Historical discharge records were analyzed using statistical frequency techniques, where the Log-Pearson Type III model exhibited the best fit with a correlation coefficient of 0.93. Comparative evaluations of AI models revealed that ANFIS achieved the highest performance, with correlation values exceeding 0.95, making it the most suitable for forecasting nonlinear streamflow behavior. Flood inundation maps generated through HEC-RAS identified high-risk zones across the basin, enabling risk-based prioritization for disaster preparedness and water resource planning. Unlike earlier studies that applied either statistical or AI-based methods in isolation, this research demonstrates the synergistic integration of AI-driven soft computing and physics-based hydrodynamic modeling. The results confirm that AI-enabled hydrodynamic modeling can deliver robust, scalable, and cost-effective solutions for flood forecasting and management. Beyond academic contributions, the framework has direct applications in IoT-based real-time monitoring, early warning systems, and decision support, offering practical tools for building climate resilience in flood-prone regions.