For successful modelling of PV system characteristics, an accurate PV module model is necessary. This research, which investigates and compares three PV module models: the single diode model (SDM), the double diode model (DDM), and the triple diode model (TDM), employs modern AI-based methods. Metaheuristic optimization techniques have solved the difficulty of obtaining parameters for the equivalent circuit of a solar module. Two artificial intelligence methods, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are employed to achieve the optimal solution for error minimization between measured and estimated data using the three models. The optimization process involves minimizing the Root Mean Squared Error (RMSE) between measured and estimated data. A convergence comparison between GA and PSO after 50 runs demonstrates that both techniques effectively extract parameters for all three models. The optimization findings demonstrate accurate modelling with RMSE results as follows: for the SDM, 5.49% (GA) and 1.02% (PSO); for the DDM, 3.91% (GA) and 0.79% (PSO); and for the TDM, 4.94% (GA) and 0.94% (PSO). These results validate the effectiveness of AI-based methods for parameter extraction, with the DDM with PSO method showing superior accuracy and timing.