This study aimed to develop a regression-based model for predicting tool life in manufacturing environments, with goals of enhancing productivity and reducing costs. In machining operations, particularly roughing processes, high cutting forces can accelerate tool wear, often leading to process interruptions and increased defect rates. Previous research on tool life prediction has frequently relied on empirical models and statistical methods, which face limitations in reliability across diverse machining conditions. To address this issue, we proposed a data-driven approach that could collects tool wear data under varying machining conditions (such as cutting speed, feed rate, and depth of cut) and applied regression models to predict tool life effectively. The model’s performance was validated under multiple conditions to assess its predictive accuracy. This study offers a practical tool life management solution for manufacturing settings, optimizing tool usage and enhancing operational efficiency.