Optimization of machining processes, such as milling, is essential for industrial efficiency and product quality. To achieve greater efficiency, it is necessary to understand how tools wear down in different conditions in order to anticipate possible undesirable events like sudden breakage or unpredictable degradation. This study focuses on understanding tool wear in dry milling of compacted graphite iron (CGI) EN-GJV-450 using PVD-coated cemented carbide and cBN tools to predict tool life effectively. The research builds on the Colding model, an empirical framework for tool life estimation, by incorporating and comparing two chip thickness concepts in order to optimize the Colding model’s performance, maximum chip thickness (hmax) and equivalent chip thickness (he). Through systematic experimentation and modelling, this work has identified optimal conditions for tool life prediction, with hmax offering a potentially resource-efficient cross-validation alternative aligned with sustainability goals. The results demonstrate that the optimized Colding model effectively predicts tool life for both coated cemented carbide and cBN cutting tools with round geometry in dry milling of CGI. The insights gained further enhance our understanding of the milling process and provide a solid foundation for selecting appropriate machining parameters to extend tool life and improve process efficiency.