The process of discovering and acquiring energy is greatly reliant on energy sources. The presence of solar energy renders it highly advantageous. The seasonal variation in weather impacts the capacity of solar energy. There is a selection of solar panels with different capacities, which depend on the behaviour of their electrical and physical qualities. Consequently, alterations in the parameters of solar panels also impact the quantity of solar energy collected, as the maximum power point (MPP) fluctuates. Prior to incorporating a photovoltaic solar panel into an energy harvesting system, it became imperative to ascertain its MPP to achieve the most efficient power generation. Techniques such as incremental conductance (IC), particle swarm optimization (PSO), perturb & observe (P&O), and artificial neural network (ANN) assist in accurately identifying the MPP in photovoltaic (PV) solar generation. The efficacy of training a PV solar panel MPP for output parameters using various training algorithms based on ANN has been assessed. To show the performance, Bayesian regularization (BR) and Levenberg–Marquardt algorithms (LM) has been utilized. When employing the LM and BR techniques, there are discernible disparities in the performances. The examination’s results, which indicate a decline in performance, elucidate these performances.