program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses.  By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques.  Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage.  The paper offers a thorough synthesis of current developments, contrasting ML-based testing with conventional methods in a number of areas, including efficacy and efficiency in defect identification.  It also highlights important research gaps, talks about real-world implementation issues, and looks at multidisciplinary uses of machine learning technologies, such as deep learning and reinforcement learning. The paper concludes by highlighting machine learning's revolutionary influence on software testing procedures and projecting a time when testing will become more independent, flexible, and incorporated into ongoing software development processes.

adaptive leadership, organizational change, crisis management, employee retention, workplace innovation

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