<p>The nonlinear and coupled nature of the wind turbine system and the high level of uncertainty associated with wind speed necessitate highly efficient control strategies. To cope with these challenges, this paper presents a novel artificial neural network (ANN)-based fractional order PID (FOPID) control strategy wherein the FOPID controller is connected to five exclusive parallel artificial neural networks and takes advantage of these networks to autonomously tune all of its parameters. This neurocontrol strategy is designed based on a radial basis function (RBF) network structure with a Levenberg–Marquardt online learning algorithm. In this manner, the resilience of the control framework with respect to environmental changes is enhanced. Meanwhile, in order to ensure the optimality of the controller based on a predefined cost function, opposition-based particle swarm optimization is employed to improve the fine tuning characteristics of neural networks. The proposed optimal neurocontroller has been tested on the two-mass model of Control Advanced Research Turbine (CART) wind turbine under a variety of wind speed scenarios, and then the validity of its performance and robustness has been verified via the National Energy Laboratory (NREL)’s FAST wind turbine simulator. The obtained results provide evidence of the superior performance achieved by the proposed neural fractional order PID (NFOPID) controller in terms of power stability and reliability, as well as mitigating fatigue damages in the benchmark wind turbine model. Furthermore, the model-free nature of the approach eliminates the need for precise mathematical modeling, making it highly adaptable to different wind turbine systems.</p>