Abstract. The variation of tool performance and nonuniform process parameters in metal forming are some of the factors that complicate the tool life modeling and analysis of such processes. In this work, a brief discussion about machine learning in analyzing metal extrusion process as well as tool life modeling, and an implemented work of using machine learning to predict failure modes for H13 Steel die used in 6063 Aluminum hot extrusion process is presented. The analysis is conducted on a set of steel dies used in 6063 aluminum hot extrusion process. The data for the failed dies used in this work is collected from a local hot extrusion manufacturer. Using artificial neural network, the prediction of the die failure modes was modeled. Moreover, the model’s accuracy and improvement recommendations are presented.