This paper evaluates the performance of an Adaptive Neuro-Fuzzy Inference System integrated with Proportional-Integral (ANFIS-PI) controllers, compared to conventional Proportional-Integral (PI) controllers, for managing turbine speeds in hydroelectric power systems. Using MATLAB for simulation, both controllers were tested under varying load conditions to assess factors such as stability, response time, and overshoot. By combining neural network learning with fuzzy logic, the ANFIS-PI controller enhances adaptability and control precision in real-time scenarios. The findings show that the ANFIS-PI controller reduced the overshoot by 35% and improved response time by 50%. This demonstrates the ANFIS-PI controller’s ability to significantly enhance operational performance in hydroelectric plants, particularly as they adapt to the fluctuating demands of power grids with increasing renewable energy sources. This implies that a system improving the turbine’s mechanical stability was produced by combining an ANN with the PI controller. The study emphasizes the need to modernize hydroelectric control systems by incorporating intelligent, adaptive controllers to improve both energy efficiency and grid stability.