An approach based on a Takagi Sugeno (T-S) fuzzy model is used to track the maximum power point (MPP) of a photovoltaic (PV) system. Specifically, an adaptive neuro-fuzzy inference system (ANFIS) is used to identify the optimized reference current. An H∞ fuzzy controller is subsequently derived using a set of linear matrix inequalities (LMI). This innovative control scheme demonstrates outstanding performance under various environmental conditions, leveraging training data from a dynamic model of the photovoltaic (PV) module. Even amidst changing climatic conditions, the desired control exhibits commendable performance in terms of tracking accuracy, energy gain factor, and tracking speed. Simulation results illustrate that the proposed hybrid strategy generates cost-effective power while efficiently harnessing renewable energy sources without wasting available energy. When compared to certain existing methods, the suggested strategy outperforms established maximum power point tracking (MPPT) algorithms (e.g., P/O and ANN). Consequently, the proposed approach ensures the most feasible energy harvest for charging a lithium-ion battery under non-uniform environmental circumstances.