The research shows that bio-inspired metaheuristics, such as the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), the Bat Algorithm (BAT), and the Frog Leaping Algorithm (FLA), can make the Perturb and Observe (P&O) algorithm work better in control of photovoltaic systems. These bio-inspired algorithms are able to optimize the parameters of the P&O algorithm more efficiently, leading to improved performance and increased energy production in photovoltaic systems. By leveraging natural elements, these metaheuristics provide innovative solutions that enhance the efficiency and reliability of renewable energy technologies. These techniques address the limitations of traditional methods by maximizing energy extraction more effectively, especially in environments with significant parameter variability. Using Matlab to design different components of a PV system and control algorithm for maximum power tracking. Additionally, Matlab allows for simulation and analysis of various scenarios to optimize the performance of the photovoltaic system. This comprehensive approach enables engineers to fine-tune the system for optimal energy production under different conditions. Simulation results show these metaheuristic algorithms enable faster energy extraction and more efficient performance.