Machine learning has become a critical tool in software testing, significantly enhancing the detection of anomalies and defects, which are crucial for maintaining high-quality software. By integrating machine learning algorithms such as supervised, unsupervised, and semi-supervised learning, companies can automate and streamline various testing processes, including anomaly detection and defect classification, which traditionally relied on manual intervention. This technological advancement enables proactive identification of issues, improves test accuracy, and reduces human error. Machine learning models can handle complex datasets and adapt to changing conditions, providing deep insights and predictive analysis that empower data-driven decision-making. As the software market demands faster and more reliable product releases, the adoption of machine learning in testing processes is expected to continue growing, offering benefits like enhanced efficiency, better resource management, and ultimately shifting from reactive to proactive testing models. This shift not only leads to improved software quality but also promises cost-effective results, aligning seamlessly with modern CI/CD pipelines for more dynamic and responsive software development workflows.