In this study, we proposed a methodology for predicting tool wear in the turning process using the SVR model. This model maintains stable performance even in small-scale data environments and demonstrates robust characteristics against outliers. We detected changes in machining performance caused by tool wear through an AE sensor and accelerometer. Features were extracted from the acquired sensor signals and utilized in the machine learning model. Prior to training, the extracted features underwent a preliminary screening process based on distance correlation. By optimizing the feature combination using the RFECV algorithm, we achieved a prediction accuracy of R² = 0.95. The analysis revealed that key features influencing the tool wear prediction model included several significant variables. Additionally, we found that evaluating feature importance allowed for more efficient model improvement. Overall, when developing a tool wear prediction model for cutting, it is crucial to utilize various sensor signals, extract features in both the time and frequency domains, and optimize the combination of those features.