The efficacy of photovoltaic (PV) systems significantly depends on optimizing power extraction under diverse environmental circumstances. The examination of Maximum PowerPoint Tracking (MPPT) approaches, particularly those utilizing soft computing (SC), is essential. They provide adaptability and accuracy for nonlinear situations such as partial shade and swift variations in irradiance. This article evaluates and contrasts SC-based MPPT methodologies, encompassing Fuzzy Logic Control (FLC), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Evolutionary Algorithms (EA), emphasizing their distinct characteristics, benefits, and drawbacks.