Water resource management in hydrological systems, particularly those involving reservoirs, is complex due to their dynamic and interconnected nature. Traditional approaches often struggle to adapt to changing conditions, resulting in suboptimal utilization and limited resilience to unexpected scenarios. This research investigates the application of AI-driven planning to enhance adaptive management strategies for such systems. This study focuses on the development and simulation of models capturing the dynamics and constraints of reservoir-based hydrological systems. These models can also be employed by automated planners to simulate system behavior. The approach is illustrated through a case study on Vietnam’s Red River Basin, demonstrating its feasibility and effectiveness. The results highlight the potential of AI planning to efficiently navigate decision spaces and derive robust management policies.