Photovoltaic (PV) systems are increasingly becoming a vital source of renewable energy due to their clean and sustainable nature. However, the power output of PV systems is highly dependent on environmental factors such as solar irradiance, temperature, shading, and aging. To optimize the energy harvest from PV modules, Maximum Power Point Tracking (MPPT) algorithms are employed to continually track the maximum power point (MPP) of the PV system under varying conditions. MPPT controllers are widely classed as either standard or optimized. Previous approaches are straightforward, but they are inefficient because they cannot discriminate among localized and worldwide summits when partial shading happens. The utilization of AI based algorithms, hybrid approaches, advanced sensor technologies and shading mitigation strategies promises to significantly improve the efficiency and effectiveness of PV system contributing to the widespread adoption of renewable energy and a more sustainable future. As a result, this research presents a succinct categorization and assessment overview of MPPT techniques used in PV systems. According to the survey results, meta-heuristic algorithms are fast and exact in monitoring GMPP amid partial shading and rapidly varying sun exposure.