In photovoltaic (PV) systems, efficient energy extraction is critical, especially under partial shading conditions where multiple local maxima can complicate the search for the true Maximum Power Point (MPP). This paper presents a robust approach to Maximum Power Point Tracking (MPPT) using the Particle Swarm Optimization (PSO) metaheuristic, tailored specifically for partial shading scenarios. The PSO method is advantageous due to its ability to handle the complex, non-linear nature of the power-voltage (P-V) and current-voltage (I-V) characteristics under varying irradiance levels. Unlike conventional MPPT techniques, which may converge to local maxima, the PSO-based method dynamically adjusts the duty cycle of the DC-DC converter, efficiently navigating the search space to locate the global MPP. The proposed method is evaluated through extensive simulations, where it consistently demonstrates superior performance in tracking the true MPP, regardless of the shading pattern. The paper provides a detailed analysis of the P-V and I-V curves under different shading conditions, showcasing how the PSO algorithm outperforms traditional methods in both convergence speed and accuracy. The results indicate a significant improvement in the power output of the PV system, highlighting the effectiveness of PSO in optimizing energy harvest. This study contributes to the growing field of renewable energy by offering a reliable and efficient solution for maximizing power generation in partially shaded PV systems.