The precise estimation of motor speed is crucial for a wide array of industrial applications. In our project, we propose an innovative method for sensorless speed estimation of Brushless Direct Current (BLDC) motors, leveraging Artificial Intelligence (AI) techniques to circumvent the need for conventional sensor feedback. To ensure the accuracy and dependability of our findings, we have adopted a comprehensive approach in our study. Alongside the development of artificial neural network (ANN) models using Python, we have constructed a complementary mathematical model using MATLAB. The MATLAB model functions as an independent validation tool, allowing us to cross-validate the results obtained from the ANN models. By integrating the fundamental principles governing BLDC motor dynamics—such as electromechanical equations and voltage-current-speed relationships—we have constructed a detailed mathematical representation within MATLAB. The proposed model includes important characteristics including motor constants, electrical and mechanical qualities, and external torque influences to provide realistic modeling of real-world events. We investigate various operating situations using simulations made possible by this model in order to accurately predict motor speed. We compare the speed estimates produced by our MATLAB scripts with the results of our ANN models by doing simulations. Any differences between these outcomes are carefully examined to find any inconsistencies or areas that need improvement. This dual strategy not only confirms the accuracy of our speed estimation procedure but also provides insightful information on the advantages and disadvantages of mathematical and ANN-based modeling approaches. Our goal is to create a solid foundation for sensorless speed estimation in BLDC motors by means of extensive.