This paper presents a comprehensive analysis of power quality in a distributed generation (DG) system utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Unified Power Quality Conditioner (UPQC). The integration of distributed generation resources, such as solar and wind power, into the electrical grid has posed significant challenges related to power quality, including voltage sags, swells, harmonics, and reactive power issues. To address these challenges, the proposed system employs ANFIS for adaptive and precise control, enhancing the performance and stability of the DG system. The UPQC is integrated to mitigate power quality disturbances by simultaneously compensating for voltage and current harmonics and providing voltage regulation. Detailed simulations are conducted to evaluate the effectiveness of the ANFIS-based control strategy and the performance of the UPQC in various operating conditions. The results demonstrate significant improvements in power quality metrics, highlighting the potential of this approach for efficient and reliable integration of distributed generation into modern power systems. The simulation findings are thoroughly examined across multiple operational scenarios and compared to Fuzzy logic control. Furthermore, the proposed system's efficacy is validated in accordance with the IEEE 1547 and IEEE 519 standards, demonstrating its performance and compliance with industrial needs.