Software has become integral to daily life, continually expanding with increasingly complex features to meet growing expectations. However, managing these complexities—from understanding application dependencies to ensuring reliability through rigorous testing—poses significant challenges.  For organizations, delivering robust and dependable software is crucial for maintaining business success and reputation. Therefore, a significant portion of the software development life cycle is dedicated to testing. Engineers often write thousands of test cases to validate new features and prevent regressions. Despite these efforts, current regression testing methods are inefficient, as they often require developers to run all test cases irrespective of whether the code paths have been altered.  This paper proposes an AI-driven approach to optimize regression testing. By analyzing specific trends, the AI identifies and prioritizes the most relevant test cases, thereby reducing execution time and resource consumption. This approach promises to mitigate inefficiencies associated with traditional testing methods, offering a more cost-effective and timely solution for software development cycles.