With the advancement of industrial electrification and automation, electric motors, as the core components of power systems, directly influence the overall performance and reliability of the system. Traditional PI controllers often fail to meet high-dynamic control demands due to their slow response speed and poor disturbance rejection capability. To address this issue, this paper proposes a speed control method for brushless DC motors that integrates an enhanced Harris hawks optimization (EHHO) algorithm with a PI controller. The proposed method incorporates opposition-based learning, adaptive weights, nonlinear energy factors, and embedded smoothing techniques, significantly improving the optimization search capability and accuracy while enhancing the control system’s adaptability to dynamic operating conditions. Through Simulink simulations and hardware experiments based on a GD32F103RCT6 control platform, the performance of EHHO-PI is compared with that of traditional PI control in various complex conditions, including sudden acceleration, sudden load, and variable speed/load scenarios. The results demonstrate that the EHHO-PI controller outperforms the conventional methods in terms of speed response, disturbance rejection, and regulation accuracy, showing strong robustness and practical applicability. This control strategy provides an effective optimization solution for motor control technology with broad engineering application prospects.