By generating several local maxima in the power-voltage (P-V) characteristic curve, Partial Shading Conditions (PSC) significantly affect the performance of photovoltaic (PV) systems, hence complicating Maximum Power Point Tracking (MPPT). For Maximum Power Point Tracking (MPPT) in solar systems under partial shade conditions (PSC), this study presents a comparative examination of two nature-inspired metaheuristic algorithms Particle Swarm Optimization (PSO) and Polar Optimization (PO). Diverse actual-world shading conditions are applied to evaluate algorithm performance by means of a simulation model built in MATLAB/Simulunk using a single-diode photovoltaic module. Examined are critical parameters such as convergence velocity, tracking accuracy, stability, and computation cost. Results show that although PSO shows quick convergence, it is sensitive to parameter change and could struggle under complex shading. On the other hand, PO's radial and angular search approach gives it better tracking accuracy and faster convergence, especially in extreme PSC situations. The findings highlight PO's efficiency as a reliable and efficient MPPT technique for PV systems in locations with fluctuating shade.