The integration of distributed generation systems (DGS) presents challenges related to maintaining high power quality in electrical grids. Traditional methods for power quality improvement often fall short in addressing the dynamic and decentralized nature of DGS. Machine learning (ML) algorithms offer a promising solution by leveraging data-driven approaches to optimize power quality. This paper presents an examination of machine learning algorithms for power quality improvement in DGS. Various Techniques within the domain of machine learning, including SVM, ANN, and decision trees, and reinforcement learning are discussed in the context of load forecasting, fault detection, harmonic mitigation, optimal control of energy storage systems, and smart grid coordination. Additionally, the paper highlights the potential benefits of ML algorithms in enhancing grid reliability, stability, and efficiency while accommodating the increasing penetration of renewable energy sources. Case studies and future research directions are also discussed to illustrate the practical application and ongoing advancements in this field. Overall, machine learning emerges as a promising tool for addressing power quality challenges in distributed generation systems, contributing to the advancement of a more robust and sustainable electrical grid.