Agile software development emphasizes short release cycles, continuous integration, incremental delivery, and rapid adaptation to changing requirements. Although these practices improve responsiveness, they substantially increase the frequency and complexity of regression testing because previously validated functionality must repeatedly be reassessed after code modifications. Conventional regression-testing strategies frequently rely on static test suites, manually defined prioritization rules, and deterministic execution policies, which can become inefficient as software systems evolve. This research presents a conceptual artificial intelligence-based regression testing framework for Agile software development that integrates machine-learning-assisted test selection, clustering, prioritization, and adaptive execution. The framework is theoretically positioned around unsupervised learning, clustering, data-driven validation, and risk-oriented decision making. The provided literature demonstrates the applicability of unsupervised machine learning to complex pattern discovery, clustering validation, and data-science workflows, while research on artificial intelligence emphasizes the importance of computational and ethical considerations when AI is introduced into sensitive decision processes. The proposed framework consequently treats regression testing as a dynamic decision problem rather than a fixed execution task. A structured methodology is developed covering test-data preparation, feature extraction, test-case clustering, risk prediction, prioritization, execution feedback, and continuous model refinement. The analytical findings indicate that AI can improve the scalability and adaptability of regression testing when test selection is driven by historical behavior, code-change characteristics, dependency information, and execution outcomes. However, model uncertainty, training-data quality, explainability, computational overhead, and inappropriate clustering assumptions remain significant limitations. The study concludes that AI-based regression testing is most effective when implemented as a human-supervised, feedback-oriented component of the Agile quality-engineering pipeline rather than as a fully autonomous replacement for established testing practices.