<p>Photovoltaic (PV) systems play a vital role in the global transition toward sustainable energy by converting solar radiation into electricity with low environmental impact. However, their performance is highly sensitive to varying weather conditions and shading, which can significantly reduce energy yield and undermine sustainability benefits. Maximum power point tracking (MPPT) strategies play a crucial role in optimizing energy harvesting, improving system efficiency, and maximizing the environmental return of PV investments. This paper provides a systematic, structure-oriented review of MPPT techniques, encompassing conventional, artificial intelligence, metaheuristic, and hybrid approaches, assessed based on key factors such as convergence speed, steady-state oscillations, tracking efficiency, computational complexity, sensor requirements, performance under different shading patterns, and parameter tuning. A major contribution of this paper is the comprehensive analysis and integration of shading-detection strategies that accurately distinguish between uniform and partial shading, enabling adaptive selection of MPPT techniques to minimize energy losses. From a sustainability perspective, improved MPPT performance directly translates to higher renewable energy generation, reduced reliance on fossil fuels, and lower greenhouse gas emissions per installed PV capacity. Hybrid MPPT methods designed for different shading conditions exhibit improved tracking efficiency and faster convergence compared to conventional techniques. The results offer practical guidance for selecting MPPT methods according to project objectives and sustainability constraints, while also highlighting important directions for future research aimed at maximizing the environmental and developmental benefits of PV systems.</p>