Efficient inventory management in various industries relies on effective spare parts classification. In this study, the Support Vector Machine (SVM) algorithm is employed as a machine learning classification method to categorize spare parts inventory. The objective of the study is to enhance the understanding of spare parts classification and provide practitioners with valuable insights for informed inventory management decisions. By applying the SVM algorithm to a dataset of 500 spare parts and conducting a thorough analysis, this study provides insights into the potential applications and limitations of the method. The findings highlight the SVM classifier model's notable predictive accuracy.