Maximum Power Point Tracking (MPPT) plays a crucial role in extracting optimal power from solar photovoltaic (PV) systems in extreme weather conditions. Traditional MPPT algorithms often suffer high oscillations, slow convergence time, and low performance during rapid environmental fluctuations. This research presents an Artificial Neural Network (ANN)-based MPPT technique for modeling and simulation in MATLAB/Simulink environment to observe these limitations behavior. The proposed model is trained with long term datasets with the parameters of Global Horizontal Irradiance (GHI), and Ambient Temperature (T ). To predict the optimal duty cycle (D), voltage and current inputs data are used, which enable fast and accurate tracking of maximum power point (MPP). An extensive solar PV model is developed in MATLAB/Simulink, that incorporates long term trained ANN- based MPPT controllers with standard solar PV features. The simulation results with long term average data and rapidly changing environmental conditions demonstrate that the ANNbased MPPT model outperforms conventional models in terms of accuracy, oscillations, tracking speed, and overall efficiency. Proposed model results were compared with traditional P&O MPPT model. The study ensures the potential of the ANN-based MPPT controllers for optimal power harvesting for nextgeneration solar PV systems and provide directions for researchers and engineers of hardware implementation.