Software quality engineering is undergoing a transition from predominantly rule-based test automation toward intelligent, adaptive, and data-driven validation. Traditional automation improves execution speed and repeatability, but its effectiveness becomes constrained when software systems exhibit changing interfaces, complex dependencies, large state spaces, and continuously evolving requirements. This research develops a conceptual framework for integrating artificial intelligence (AI) with software quality engineering by interpreting test automation as an adaptive control and optimization problem. The methodological foundation is derived exclusively from the supplied literature, particularly studies concerning motion control, multicriteria optimization, adaptive behavior, energy-efficient operation, stability, and modeling of complex interacting systems. Although these references originate primarily in robotics and mechanics, their theoretical constructs provide transferable principles for intelligent software testing. The proposed framework combines test-case generation, prioritization, execution monitoring, defect-risk assessment, feedback-based adaptation, and optimization into a closed quality-engineering loop. The analysis indicates that AI-enhanced automation can be conceptually understood as a system that observes software behavior, evaluates quality states, selects testing actions, and continuously updates its testing strategy. The study further identifies limitations associated with model uncertainty, training data quality, explainability, false positives, computational overhead, and inadequate validation criteria. The resulting framework provides a theoretically grounded basis for designing next-generation automated testing systems while highlighting the need for empirical validation on real software-development environments.