The exponential growth of software systems and the shift toward Agile and DevOps methodologies have placed immense pressure on quality assurance teams to deliver rapid, reliable, and scalable testing solutions. Traditional test automation methods, while effective in some contexts, often struggle with adaptability, maintenance overhead, and limited intelligence in handling dynamic test environments. This paper investigates the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in shaping the future of software test automation. Through an in-depth analysis of technical mechanisms and current tool capabilities, this study identifies core AI/ML techniques such as predictive analytics, computer vision, reinforcement learning, and natural language processing as critical to addressing the limitations of conventional test automation. The research highlights the evolution from rule-based testing to intelligent, self-healing, and autonomous testing frameworks. Quantitative comparisons reveal substantial improvements in test efficiency, coverage, defect detection accuracy, and maintenance costs. Furthermore, the paper outlines key implementation challenges, including data dependency and model drift, and offers strategic recommendations for organizations. The findings establish that AI/ML is redefining the foundations of software quality, moving toward faster releases and sustainable testing practices for the next generation of software engineering.