Regression test suite growth in software development is a problem that this study attempts to solve. Test case prioritization, regression test suite analysis, and bagging are all combined using machine learning. Also samples are collected through sensory nodes for validation purpose. Dependency analysis, code coverage, and historical failure statistics are some of the criteria used by the method to assess the regression test suite. Utilizing machine learning methods such as Random Forest, the chosen test cases are ranked according to how likely they are to fail. By combining the predictions from various machine learning models, the bagging method improves the prioritization process. Higher error rate test cases are prioritized, which reduces the size of the suite and improves regression testing effectiveness, according to experiments conducted on real-world software projects.