Accurate parameter extraction for photovoltaic (PV) models is essential to optimize the performance and reliability of solar energy systems. In this paper, we propose a Modified Grey Wolf Optimizer (M-GWO) for extracting key parameters of the Double Diode Model (DDM) of the RTC France solar cell. DDM provides a more comprehensive representation of PV cell behavior by considering both recombination and diffusion effects, making parameter extraction more challenging due to the model’s inherent non-linearity. The M-GWO improves upon the standard Grey Wolf Optimizer (GWO) by incorporating the Dimension Learning-Based Hunting (DLH) strategy, inspired by individual hunting behavior in nature. DLH expands global search capacity through multi-neighbor learning, enhancing exploration and exploitation balance, while mitigating premature convergence. Comparative simulations were performed using other metaheuristic algorithms, including GWO, Particle Swarm Optimization (PSO), Differential Evolution (DE), Atomic Orbital Search (AOS), and Multi-Verse Optimizer (MVO). The performance of these algorithms was evaluated based on key metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and execution time. All simulations were conducted using MATLAB Simulink to ensure rigorous analysis. Results demonstrate that the M-GWO surpasses the original GWO and other algorithms in terms of precision, speed, and overall efficiency in parameter estimation. This study highlights the potential of M-GWO as a robust tool for parameter optimization in complex PV models, paving the way for broader applications across different PV technologies.