Brushless DC (BLDC) motors are gaining significant attention due to their superior performance characteristics, including high efficiency, long operational lifetime, and enhanced dynamic response. However, challenges such as torque ripple and fault tolerance remain critical issues. This paper presents an advanced control strategy integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Space Vector Pulse Width Modulation (SVPWM) to optimize the performance of BLDC motors. The primary objective is to minimize torque ripple and improve speed regulation, particularly under varying load conditions. The proposed method dynamically adjusts the proportional-integral-derivative (PID) gains using ANFIS-based tuning, ensuring optimal control adaptability. Simulation results in MATLAB Simulink demonstrate significant improvements in speed tracking and torque ripple minimization compared to conventional PID-PWM and PID-SVPWM controllers. Furthermore, the system’s performance is validated through hardware implementation, confirming the effectiveness of ANFIS-SVPWM in real-time applications. The findings highlight the potential of AI-based control strategies in enhancing BLDC motor efficiency, making them highly suitable for marine and industrial applications.