This paper presents a novel approach to mechanical tools classification within the framework of Industry 4.0, focusing on the use of machine learning to automate tool identification in industrial settings. The objective of this work is to develop a reliable classifier that can accurately categorize various mechanical tools, thereby streamlining manufacturing processes and reducing the potential for human error. To achieve this goal, we collected a comprehensive dataset consisting of mechanical tool characteristics, including size, shape, and operational context. The classifier was trained using this dataset, employing robust machine learning algorithms to ensure high accuracy and adaptability. To validate the classifier, we conducted extensive testing in both controlled and real-world industrial environments. The results demonstrate that the classifier achieves high precision and recall rates, significantly improving the efficiency of tool identification and categorization. This automation has the potential to save considerable time and resources in manufacturing processes, as well as enhance overall productivity.