Machine learning (ML) has emerged as a transformative force in predictive software quality assurance (PSQA), offering data-driven techniques for early defect detection, test case prioritization, and code quality analysis. This survey explores the current landscape of ML-driven approaches in PSQA, emphasizing the use of supervised learning, deep learning, and natural language processing (NLP) to enhance automation and precision in software testing. The paper compares traditional quality assurance methods with modern ML-based techniques, highlighting their advantages and limitations. Key trends such as the integration of ML models into CI/CD pipelines, the adoption of transformer-based models, and the application of ensemble learning are examined in depth. Additionally, challenges related to data quality, model interpretability, and scalability are discussed. The paper concludes by identifying future research directions, including the need for explainable AI, domain-specific datasets, and adaptive learning frameworks. This review aims to guide researchers and practitioners in leveraging machine learning to build more efficient, scalable, and intelligent quality assurance systems.