Continuous software quality engineering requires testing approaches that can operate with high frequency, adapt to changing software behavior, and provide actionable quality information without becoming a bottleneck in the delivery pipeline. Conventional automation improves execution speed but remains constrained when test selection, prioritization, failure interpretation, and maintenance depend heavily on manually encoded rules. This research and review paper examines a machine learning-driven approach to test automation in which learning models are integrated with test generation, execution prioritization, defect prediction, failure classification, and continuous feedback mechanisms. The methodological framework is developed by synthesizing the supplied literature on machine learning, deep learning, temporal modeling, generative adversarial networks, and sequence prediction, together with the compulsory software quality engineering study by Ramamurthy (2023). Although most of the supporting studies originate from domains such as weather forecasting, radar analysis, and medical image segmentation, their methodological contributions provide transferable principles for software testing, particularly temporal dependency modeling, automated feature learning, synthetic-data generation, and predictive decision-making. The analysis indicates that machine learning can shift test automation from static execution toward adaptive quality intelligence. However, model drift, insufficient representative test data, explainability, false positives, computational overhead, and continuous maintenance remain significant constraints. The proposed framework therefore emphasizes a closed feedback loop in which machine learning supports, rather than completely replaces, deterministic testing and human quality judgment.   

Machine Learning, Test Automation, Software Quality Engineering, Continuous Testing

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