The efficient operation of Solar Photovoltaic (PV) cells depends how precisely the modelling of its characteristics has been done. Hence, accurate parameter estimation of solar cells is crucial to closely replicate their real-world performance. In this paper, we propose a novel hybrid approach (SGANMDE) that combines the Snow Geese Algorithm (SGA), Nelder-Mead (NM) algorithm and Differential Evolution (DE) algorithm for parameter estimation of solar PV models. The proposed sequence effectively employs global search of SGA followed by local refinement of NM and finally secondary search by DE to further minimize Root Mean Square Error (RMSE). We evaluate the proposed method's performance by applying the hybrid algorithm on different PV models to minimize the RMSE value. Results are presented by means of I–V curve for each PV model to visually depict how accurately the model’s values are matching the experimental value which demonstrates the proposed hybrid algorithm’s ability to minimize estimation errors across diverse datasets. Also, by means of convergence curve it is demonstrated how each method (i.e. SGA, NM and DE) contributes to improve the solution by reducing the RMSE in a stepwise manner, and arriving at the best possible solution.