Hydroelectric power plants necessitate precise measurement of water flow velocity in their inlet pipes to optimize water resource management. While various devices such as flow sensors have traditionally been used for this purpose, our research delves into the operational dynamics of water turbines. By deriving characteristic equations of turbine blades using a design constant ‘K’—a coefficient relating blade angular velocity to flow velocity—we aim to enhance flow velocity estimation accuracy. Our methodology involves conducting rigorous experimental tests within a wind tunnel setup. Subsequently, we leverage advanced computational techniques, including the training of neural networks, to develop a robust mathematical model. This model utilizes input parameters such as blade angular velocity and fluid temperature to predict flow velocity. Through rigorous validation against Computational Fluid Dynamics (CFD) simulations, our approach ensures high-fidelity flow velocity estimation for improved hydroelectric plant efficiency and resource optimization.