This study utilizes suggestive R codes to predict bank employee productivity based on Time Management techniques. It presents a data-driven approach, employing statistical analysis and machine learning. The study uses a data visualization and predictive modeling methodology to identify correlations between time management practices and employee productivity. The predictive models demonstrate high accuracy in forecasting employee productivity. The analysis of this framework reveals trends and patterns in time utilization, including peak productivity hours and time-wasting activities. Effective time allocation, task prioritization, and minimizing distractions are essential to employee efficiency. The study contributes to existing literature, offering a novel R-based approach. Its methodology can be easily applied to various industries, making it a valuable resource for researchers and practitioners seeking to optimize time management and boost employee productivity. The study's findings have practical implications. The conclusion includes unique and intuitive suggestions that can be incorporated as an extension of this study.