Continuous integration and delivery (CI/CD) have transformed software development by reducing delivery time, improving product quality, and giving enterprises a competitive advantage. However, large-scale projects confront difficulties in giving fast feedback to developers due to large test suites, resulting in longer testing cycles and lower productivity. Traditional regression testing methods struggle to find a balance between efficacy and efficiency, demanding advanced approaches. This study investigates the use of machine learning (ML), specifically Neural Network and Random Forest models, to choose test cases based on source code changes, commit messages, and change file path in order to offer developers with faster feedback. The study investigates the predicted accuracy of ML models using a large industrial dataset from a telecom company, which included 15 million test executions over 15 months. The results show that Random Forest outperforms Neural Network models in test case selection, with up to 97% accuracy achieved. Real-time evaluations conducted over a month show significant savings in test executions (88 % -90 %) and testing time (44 % -74%) across multiple regression testing activities, illustrating the potential of ML-driven techniques to optimize CI/CD pipelines and increase developer productivity.