Modern software systems are characterized by rapid release cycles, continuously changing requirements, heterogeneous platforms, and increasingly complex user interactions. These conditions create substantial challenges for conventional test automation, particularly in test-case maintenance, regression-test selection, defect prediction, and adaptation to changing software interfaces. This research examines the role of machine learning (ML) in intelligent test automation and develops a conceptual framework for integrating ML techniques into modern software quality assurance (SQA). The study adopts a research-and-review approach based exclusively on the supplied literature and uses comparative synthesis to connect existing evidence on intelligent digital systems, mobile applications, augmented-reality environments, software platforms, and AI-driven test automation. The proposed framework incorporates test-case generation, test prioritization, defect-risk prediction, failure classification, regression optimization, and continuous learning into a unified quality-assurance workflow. The analysis indicates that ML can potentially transform automation from a rule-based execution mechanism into an adaptive decision-support system capable of learning from historical test outcomes and application behavior. The study further identifies limitations involving data quality, model interpretability, changing application behavior, false predictions, and maintenance requirements. The findings position intelligent automation as a complementary evolution of conventional automation rather than a complete replacement for human quality engineers. The research contributes a conceptual architecture for applying ML systematically across the software testing lifecycle and identifies directions for empirical validation.

Machine Learning, Intelligent Test Automation, Software Quality Assurance, Automated Testing

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