The use of machine learning techniques has become a viable strategy for enhancing software quality due to the ever-increasing complexity of software systems and the demand for greater quality and reliability. This study provides an in-depth analysis of the application of machine learning in the setting of software quality, emphasizing its possible advantages, difficulties, and future directions. The first section of the paper gives a general introduction to machine learning and its core ideas, such as feature extraction, supervised and unsupervised learning, and model evaluation methods. After that, it looks at a number of software quality-related topics where machine learning approaches have been effectively used, including defect prediction, fault localization, test case prioritization, and software maintenance. The study also covers the specific machine learning algorithms—such as decision trees, support vector machines, random forests, and neural networks—that are frequently used in software quality applications. It looks at the applications of these methods for feature selection, classification, clustering, and anomaly detection. This study discusses the benefits of machine learning for software quality as well as the obstacles and constraints faced by practitioners and researchers. The lack of labeled data, the interpretability of models, and the possibility of bias in training data are some of these difficulties.