The increasing adoption of solar photovoltaic (PV) power generation stems from its renewable and eco-friendly attributes. However, conventional Maximum Power Point Tracking (MPPT) methods encounter difficulties in efficiently harnessing power from PV systems under Partial Shading Conditions (PSC). During PSC, these systems exhibit fluctuating power outputs due to shading, leading to challenges in identifying the Global Maximum Power Point (GMPP). The presented research introduces a pioneering Co-Operative Guidance factor based Salp Swarm Optimization algorithm (CGFSSO) tailored for MPPT in PSC scenarios within PV systems. The CGFSSO method focuses on precise GMPP localization with minimized oscillations by enhancing the update mechanism and effectively exploring the expansive search space. To assess its efficacy, the proposed CGFSSO approach undergoes comparison against conventional MPPT techniques, Fuzzy logic and Optimization based MPPT methods through rigorous simulation studies. The results underscore the CGFSSO method’s exceptional performance in precisely tracking the GMPP and improving MPPT power efficiency when contrasted with established methodologies. This study signifies a promising stride towards optimizing power extraction from PV systems operating under demanding partial shading conditions.