In this paper, scaled conjugate gradient (SCG) optimization algorithm tuned artificial neural network (ANN) for grid interconnected solar PV system applications was presented. In consequence of the fact that fossil fuels will inevitably run out, researchers are searching for better, more sustainable ways to supply every individual’s energy needs. The globe has turned its focus to renewable energy sources (RESs) as RESs have great potential to solve the issue of fulfilling global power demand. Proper switching is needed to the power converter circuitry at the dc link side to make the system reliable. As the traditional approaches failed in addressing the issues of accuracy and convergence criterions, to overcome this drawback, ANNs have been emerged as the best alternative. When encountering ANNs, one of the most significant aspects is training or learning. The ANN was trained using the SCG optimization technique. Bayesian Regularization (BR) approach was adopted to compare the proposed algorithm with MATLAB/Simulink software, which was used to simulate the proposed system. The proposed system was simulated, and the outcomes stated that the SCG-based ANN controller performed better than the BR technique, making it more accurate.