Flooding remains one of the most catastrophic natural hazards, often causing widespread human casualties and extensive economic damage. Nonetheless, the severity of its consequences can be significantly mitigated through precise modeling, thorough analysis, and the implementation of effective flood management strategies. A deep understanding of flood behavior and trends is crucial for improving forecasting accuracy and enabling timely preventive actions in flood-prone areas. When integrated with early warning systems, flood control infrastructures, and coordinated emergency responses, reliable flood forecasting can dramatically reduce the risk to human life and infrastructure. This study adopts a documentary and library-based research methodology to gather and analyze relevant data, with the objective of enhancing the accuracy of flood modeling techniques. Specifically, the study evaluates the effectiveness of the Maskingham-Cunge method in conjunction with genetic algorithms for modeling flood behavior. The integration of these approaches allows for the dynamic adjustment of parameters, replacing static inputs with variable ones to better reflect real-world conditions. Additionally, incorporating one-dimensional kinematic wave theory to compute wave speed improves the precision of output hydrograph estimation. The findings demonstrate that this combined approach significantly enhances the predictive performance of flood models. As a result, it offers a robust tool for informed decision-making in flood management, contributing to more efficient disaster preparedness and risk reduction efforts in vulnerable regions.