In electrical engineering, Brushless Direct Current (BLDC) motors are frequently used in mechanical applications because of their effectiveness, strong torque, and small design. Nevertheless, reaching peak performance and making accurate adjustments to parameters can be difficult when using a simple, customized Proportional Integral Derivative (PID) controller. In the past, speed control typically included adjusting crucial factors like voltage, and current. However, manual speed regulation has drawbacks, including being time-consuming, susceptible to human error, and lacking scalability. Different traditional models have tried to enhance speed control efficiency with Artificial Intelligence (AI) but face challenges in improving Rise Time, Settling Time, Maximum Overshoot, and overall efficiency. A proposed approach to address this problem involves the implementation of a developed model using the Enhanced Whale Optimization Algorithm- Tuned PID (EWOA-TPID) Controller. This system utilizes the benefits of the Whale Optimization Algorithm (WOA) to increase convergence speed, enhance exploitation and exploration abilities, and accurate speed control by efficiently tuning the parameters of PID to decrease the steady state error and overshoot. The key performance metrics which comprise Rise Time, Settling Time, and Maximum Overshoot are utilized to assess the efficacy of this approach. Moreover, the presented system is compared with conventional models to showcase the improved effectiveness of the respective model. This innovative approach intends to contribute significantly to studies in areas like robotics, automation, electric vehicles, industrial machinery, and other systems that use BLDC motors for speed regulation.