Photovoltaic (PV) energy has become a central pillar in the global transition toward clean and sustainable power sources, offering a renewable and environmentally friendly alternative to conventional electricity generation. This study focuses on optimizing the parameters of a single-diode photovoltaic model to improve its accuracy in representing real-world behavior. A Laguerre-inspired optimization (LO) method is employed, which adjusts each parameter individually. The optimization process relies on minimizing the mean squared error (MSE) between the experimental current values and those predicted by the model, using the first and second derivatives of the objective function. The Laguerre approach's parameters are contrasted with those found in current research using analytical methods, iterative approaches, and metaheuristic algorithms. The root mean square error (RMSE), mean absolute error (MAE), and individual absolute error (IAE) are among the statistical error metrics that are computed to evaluate the performance of the suggested technique. The efficiency of the Laguerre technique in photovoltaic parameter extraction is demonstrated by this methodical approach, which enables perfect calibration of the model in line with experimental data. The suggested LO technique's robustness and dependability in this field are confirmed by the comparison study, which shows that the results produced by this approach have lower error rates than those obtained by other optimization methods.