The research paper aims to assess and enhance the performance of (PV-B-UPQC) battery fed PV integrated Unified Power Quality Conditioner in microgrid systems using an ANFIS controller. The rising need for sustainable and dependable energy solutions has driven a greater emphasis on microgrid systems, which combine solar photovoltaic (PV) arrays, energy storage, and power quality enhanced technologies. The goal of this research is to find a solution for the UPQC system's power quality problem. When the UPQC is operated using instantaneous reactive power (PQ) and synchronous reference frame (SRF) methods, total harmonic distortion of grid current can exceed 5% under extreme conditions like significant voltage swell/sag. To tackle this issue, an artificial neural network is employed to adjust the UPQC's shunt active filter effectively. The proposed model's performance is evaluated under various scenarios, including voltage sag/swell circumstances, imbalanced loads, and non-linear load situations. Simulations were done with MATLAB Simulink. The results shown substantial improvements in energy efficiency, power quality, and grid reliability, making this technology a promising outcome for enhancing micro grid system performance in the era of renewable energy. The recommended artificial neural network controller efficiently reduces power quality problems and optimizes control complexity.