This study presents an intelligent Maximum Power Point Tracking (MPPT) control strategy for variable-speed wind turbine generators, based on the Crow Search Algorithm (CSA) to maximize power generation under wind fluctuations. The proposed CSA-based MPPT method is designed to improve the dynamic response and efficiency of wind energy conversion systems by effectively tracking the optimal operating point. The performance of the CSA-based approach is compared with a conventional torque regulation method, evaluating key metrics such as convergence speed and robustness under turbulent wind conditions. Simulation results demonstrate that the CSA-based MPPT controller outperforms the conventional method, achieving faster convergence to the maximum power point, reduced power oscillations, and improved energy capture efficiency. The results highlight the potential of bio-inspired algorithms like CSA in advancing MPPT control for renewable energy systems, offering a promising alternative to traditional methods for enhancing the performance and reliability of wind turbine generators.