— These This study uses a neural network controller to develop a maximum power point tracking controller (NNC). This controller will detect and control the speed of a solar-powered 3-phase induction motor. All photovoltaic systems must use the Maximum power point tracking (MPPT) method (PV) system, and to boost the system's effectiveness, incremental efficient conductance algorithm is utilized to obtain the solar panel's maximum output, which powers an induction motor with SEPIC Converter which boosts the solar panel's available voltage. It uses a dc converter. The converter's primary benefit is having non inverted output. Between the PV array and the converter, it serves as an interface motor strain. In order to monitor the maximum power point (MPP) of solar cell modules, an improved MPPT algorithm based on neural network (NN) approach is proposed after assessing the output characteristics of a solar cell. The Bayesian Regularization approach was selected as the training strategy to complete the assignment since it performs well even for smaller data, supporting the diverse train data set. Theoretical findings demonstrate that in the same environment, the Enhanced NN MPPT algorithm is more efficient than the Perturb and Observe technique. MATLAB/Simulink software is used for designing of proposed system