Milling machines remain of great importance in modern manufacturing, with tool optimization playing a significant role in cost reduction. Insert-compound cutting tools can reduce the cost of operations by optimizing their lifespan. Subsequently, an accurate prediction of insert wearing is essential. This article analyses the wear of cutting tools in milling machines, focusing on accu-rately predicting their lifespan using artificial intelligence algorithms. The study highlights how artificial intelligence, through various models, has greatly improved the precision of wear pre-dictions, surpassing the results of prior studies. Results obtained from the dataset of an insert tool, which are widely used today, can be extrapolated to other milling inserts. The results demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. By analyzing this wear, the system can alert the machine operator when it is necessary to replace the cutting insert before failure occurs, thereby preventing suboptimal workpiece quality. This proactive approach not only enhances operational efficiency and reduces the overall cost of production but also minimizes the need for continuous supervision, contributing to more streamlined and autonomous machining processes.