This work suggests a hybrid framework to predict the dependability of concrete-filled steel tube (CFST) columns under axial stress by combining Monte Carlo Simulation (MCS), the CatBoost gradient boosting technique, and SHAP explainability. The model was trained using a dataset of 663 experimental CFST samples; the regression target was the computed failure probability Pf using Monte Carlo Simulation (MCS). Based on the dependability metric β, the CatBoost model effectively classified all samples into safety categories and achieved high predicted accuracy. According to SHAP analysis, geometric parameters—especially outer diameter, wall thickness, and column length—had the highest impact on expected failure probability. The dataset often revealed that a significant fraction fell below the accepted safety threshold \(\beta = 3.0\) , which emphasizes the importance of design review in many respects. Moreover, a decision tree classifier was constructed to extract rule-based safety reasoning, providing a precise tool for informed engineering decisions. The proposed framework offers an accurate, interpretable, and computationally efficient alternative to conventional dependability evaluation techniques, leveraging transfer learning and semi-empirical modeling. It lays a strong basis for future applications to eccentric loading situations.