Abstract: This study aims to propose and validate a method for predicting the lifecycle stage of CT X-ray tubes using short-term equipment operational data. Detailed operational logs from five CT scanners over a three-month period, along with long-term tube replacement records, were collected from a large tertiary hospital. The Kaplan-Meier survival analysis and log-rank tests were employed to screen for the optimal lifespan metric. Combined with thermal reaction equations, 15 quantitative workload features were extracted. The lifespan prediction problem was transformed into a four-stage classification task, trained, and evaluated using six machine learning models. Total scan volume and total exposure seconds reflected the tube degradation process more effectively than calendar days. The Random Forest model achieved the best performance, with a 5-fold cross-validation accuracy of 89.2% and an F1-score of 0.892. Feature importance analysis confirmed that indicators of cumulative energy consumption and usage intensity were highly correlated with the tube's lifecycle stage, a pattern consistent with physical degradation laws. Utilizing short-term operational data combined with machine learning models allows for high-precision prediction of CT tube lifecycle stages. This method provides a reliable decision-support tool for healthcare institutions to implement low-cost and efficient predictive maintenance.