<p>The development of a smart predictive monitoring system for modeling tool wear during the milling process has gained attention in the field of non-destructive testing for different material structures. The use of the Acoustic-Emission (AE) technique has been beneficial in inspecting damages in material structures. This study focuses on inspecting tool materials during the milling process and predicting the remaining useful life (RUL) of this tool under a high-speed milling machine cutter. The dataset used is available online and includes six series of cutting processes, with seven variables data signals present in each series (three for forces, three for vibrations, and one AE). The main aim of this study is to solely use the AE signals from the dataset to investigate the RUL of tool materials. The steps in processing and pre-processing the dataset involve detecting AE signals from the six cutting series, normalizing, and feature extraction. Relevant features are selected using the technique of Infinite Feature Selection (InfFS), followed by modeling of tool wear using the selected features through the online support vector regression (SVR) method, trained with three series (C1, C4, C6), and prediction of RUL based on the obtained models. The predicted models for the three tested series (C2, C3, C5) at each level of the cutting process have shown good performances, directly influencing the RUL of the tool. This developed processing approach enables the prediction of models for tool wear using only AE signals, marking a notable advancement in the field of predictive monitoring systems of materials under milling processes.</p>