IntroductionPhotovoltaic systems offer immense potential as a future energy source, yet maximizing their efficiency presents challenges, notably in achieving optimal voltage due to their nonlinear behavior. Operating current and voltage fluctuations, driven by temperature and radiation changes, significantly impact power output. Traditional Maximum Power Point Tracking (MPPT) methods struggle to adapt accurately to these dynamic environmental conditions. While Artificial Intelligence (AI) and optimization techniques show promise, their implementation complexity and longer attainment times for Maximum Power Point (MPP) hinder widespread adoption.MethodThis paper proposes a hybrid MPPT technique that integrates the Pelican Optimization algorithm (POA) with the Perturb and Observe algorithm (P&O) for a grid-connected photovoltaic system (PV). The proposed technique consists of two loops: PO as the reference point setting loop (inner loop) and POA as a fine-tuning (outer)loop. The combination of inner and outer loops minimizes oscillations by adjusting the perturbation direction and enhancing the convergence speed of the MPPT.Results and DiscussionTo validate the efficacy of the proposed MPPT technique for different environmental conditions, a comprehensive comparison is conducted between the proposed hybrid pelican and perturb and observe (HPPO) technique and other MPPT algorithms. The proposed technique has optimized PV and grid outputs with an MPPT efficiency of 99%, best tracking speed, and total harmonic distortion (THD) for all conditions below 5% agree with IEEE 519 standards.