Artificial intelligence (AI) techniques play a crucial role in providing smart and adaptive solutions to complex problems. By combining optimization, learning, and predictive capacities, the use of these methods remains relevant for enhancing the operational behavior of engineering systems. Therefore, the integration of AI techniques into energy-conversion systems has attracted significant attention recently due to their high performance, especially in photovoltaic (PV) systems, such as Standalone mode. Classical Maximum Power Point Tracking (MPPT) methods often suffer from slow convergence and reduced tracking efficiency under severe operating conditions such as Partial Shading Conditions (PSCs). However, most existing hybrid AI-based MPPT techniques rely on continuous metaheuristic optimization processes that increase computational complexity in standalone PV systems under rapidly varying PSCs. The current paper proposes an AI-combined MPPT control technique based on an Improved Perturb and Observe (IP&O) algorithm with a Grey Wolf Optimizer (GWO) for maximizing PV power extraction under these highly complex, non-uniform irradiance conditions, which emulate realistic operating conditions. Unlike traditional P&O, the suggested MPPT employs the GWO algorithm as an adaptive layer to dynamically tune the IP&O step size, enabling faster convergence while reducing the computational burden. At the same time, this improved AI-based method rapidly regulates the duty cycle of the Boost converter. The configuration of the standalone PV system comprises a PV array, a DC–DC boost converter, and a resistive DC load, regulated by the suggested GWO-IP&O approach, which is simulated in MATLAB/Simulink environment, version 2020b. The findings indicate that the proposed control technique achieves a tracking efficiency above 97%, reducing convergence time by approximately 30% and decreasing steady-state oscillations by nearly 50% compared to traditional P&O. Furthermore, it maintains the overall stability around the operating points, which significantly minimizes the PV power losses. The novelty of this work lies in its integration of the bio-inspired GWO optimization algorithm for adaptively tuning the parameters of the IP&O, enabling an improved performance. The obtained results demonstrate the superior potential of bio-inspired artificial intelligence applications, which provide a powerful solution for improving advanced MPPTs, and can be applied to achieve intelligent energy conversion in standalone PV mode in terms of robustness and reliability when exposed to complex PSCs.