Shading presents a significant challenge to the efficiency of photovoltaic (PV) panels, often resulting in suboptimal operation and reduced power generation, thereby leading to economic losses. Traditional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O) or Incremental Conductance (Inc-Cond), may struggle to effectively handle Partial Shading Conditions (PSC), where the power-voltage (P-V) curves exhibit multiple local peaks alongside a singular global peak. In contrast, metaheuristic algorithms demonstrate adaptability and efficacy in find the global maximum. This study investigates and assesses the MPPT capabilities of six optimization techniques: Particle Swarm Optimization (PSO), Moth-flame Algorithm (MFA), Cuckoo Search Algorithm (CSA), Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Grey Wolf Optimization Algorithm (GWO). Metaheuristic algorithms are recognized for their potential to converge more efficiently towards the optimal solution compared to traditional methods. This efficiency is particularly advantageous in real-time applications where rapid adaptation to changing environmental conditions is crucial for maximizing energy harvesting efficiency. The findings from this research contribute to advancing the understanding and application of metaheuristic approaches in enhancing the performance and resilience of PV systems under challenging operational conditions like Partial Shading.