Over the past few decades, there has been remarkable surge in the utilization of Solar Energy. This significant growth can be attributed to pressing concern like the depletion of conventional energy sources, the threat of global warming, and the economic downturn brought by the COVID-19 pandemic. Open circuit, short circuit, and maximum power points are typically provided by manufacturers and are used to calculate PV system parameters using data based on I-V features. This study presents a novel method for accurately modeling solar modules, with particular emphasis on the photovoltaic single-diode model (SDM) that has five unknown parameters: \(I_{{{\text{light}}}}\) , \(I_{{{\text{sat}}}}\) , \(a\) , \(R_{{{sh}}}\) , and \(R_{s}\) . This study deviates from conventional experimental methods by putting forth a novel approach to parameter extraction. In order to effectively ascertain the unknown parameters of the SDM, it offers a new meta-heuristic algorithm, Crayfish Optimization Algorithm (COA). Rather than depending solely on lengthy tests, it creates a unique cost function based on datasheet values. Two of the parameters are estimated using standard test conditions (STC) data, while the remaining three are optimized by the COA method. The COA is used to extract parameters from commercial PV panel, the KC200GT, in order to verify the efficacy of this method. The I-V curves for these PV modules are then produced for five simulation runs under STC.