The growing energy crisis, climate concerns, and depletion of fossil fuels have made it imperative to transition towards sustainable and efficient power systems. However, integrating renewable energy sources such as solar and wind poses challenges due to their variability and unpredictability, especially in maintaining stable power generation and ensuring grid compatibility. To address these issues, Hybrid Renewable Energy Systems (HRES), integrating solar photovoltaic (PV), wind turbines, and fuel cells, are increasingly deployed within smart microgrids. This integration not only improves energy reliability but also reduces carbon emissions and enhances overall system efficiency. A critical challenge in solar systems is accurate and fast Maximum Power Point Tracking (MPPT), especially under rapidly changing atmospheric conditions. Hence, this work compares conventional MPPT methods—Perturb & Observe (P&O), and Incremental Conductance (IC) to analyze their limitations in dynamic scenarios. To overcome their drawbacks, a Neuro-Fuzzy MPPT controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The ANFIS-based approach adaptively learns and adjusts control actions, achieving faster convergence and higher efficiency. Furthermore, a Boost converter and SPWM inverter are modeled for grid interfacing, and an LCL filter is introduced to mitigate harmonic distortion. Simulation results demonstrate superior power tracking and improved power quality, validating the system’s potential for reliable and intelligent grid integration.