The accurate modeling of PV modules is critical for enhancing performance of electrical grids during simulations. However, modeling PV systems requires dealing with a nonlinear current–voltage curve with unknown parameters, which is frequently due to the restricted data available in cell datasheets. This paper presents a unique optimization technique known as the Human Evolutionary Optimizer Algorithm (HEOA) for correctly predicting the parameters of triple- diode model (TDM). In this study, the HEOA optimizer minimizes the root mean square error function in order to precisely find the TDM's ideal parameters. The simulation outcomes clearly demonstrate the effectiveness of HEOA in accurately extracting model parameters. Throughout the optimization process, the HEOA consistently achieved the lowest ideal fitness values when compared to other state-of-the-art optimization algorithms, indicating superior performance. The results further confirm that the HEOA offers faster convergence rates and higher estimation accuracy, highlighting its efficiency and precision in solving complex parameter identification problems. In addition, comprehensive statistical analysis supports the robustness and consistency of the HEOA across multiple trials, reinforcing its reliability as an optimization tool. For further validation, the proposed approach will be tested using standard benchmark cases, including the RTC France Solar Cell and the KC200GT PV module, to ensure comparability with existing methods. Moreover, real-world validation will be conducted under dynamic climate conditions using the Trina Solar Monocrystalline. Then, the TDM for solar cells/modules is notable for its excellent accuracy, which accounts for both electrical and non-electrical losses. The HEOA-based TDM system achieved parameter extraction performance which matched the benchmark results and practical PV test results. The HEOA method produced the best RMSE result of 8.1307E−04 for the RTC France solar cell, which surpassed multiple recent optimization techniques. The KC200GT PV module achieved an RMSE result of 3.32E−04 which demonstrated that the method accurately reconstructed the nonlinear I–V and P–V characteristics through its extremely small pointwise current and power errors. The experimental validation showed that Trina Solar 290 W monocrystalline modules performed accurately under real operational environments, which included testing single-cell, five-cell, and six-cell configurations to achieve minimum RMSE values of 0.0 and mean RMSE values of 0.0141, 0.0059, and 0.0521. The results demonstrate that HEOA produces precise and dependable parameter estimation for PV cells and modules.