Test automation remains essential but increasingly brittle as applications evolve rapidly. This journal article introduces a semantic intelligence approach to test automation, enabling contextdriven adaptation through Natural Language Understanding (NLU), contextual reasoning, and machine learning. Unlike prior work that emphasizes locator-level self-healing alone, this study treats semantic understanding and business context awareness as first-class capabilities for resilient, self-adapting test systems. The framework learns from application behavior patterns, user workflows, and business rules to guide test execution, validation, and adaptation decisions. Through evaluation across 47 enterprise applications spanning finance, retail, healthcare, and media, we show that semantic reasoning can reduce test maintenance overhead by up to 85%, improve defect detection by 62%, and achieve 94% accuracy in autonomous UI change adaptation. A key contribution is semantic context graphs that map business intent to technical implementations, allowing tests to interpret not only “what” changed, but “why” it matters. We further introduce quantitative metrics for test-suite semantic drift and practical mitigation strategies. Finally, we discuss challenges in domain adaptation, computational efficiency, and interpretability, and propose solutions based on transfer learning and explainable AI techniques.