Concrete structure damage is commonly described through damage models, and the probabilistic calibration of these models’ parameters is essential for accurately predicting structural behavior. As the need for more efficient calibration methods grows, surrogate models have become increasingly relied upon to reduce the computational cost associated with finite element simulations. This study employs machine learning techniques to predict the structural response of concrete components with high accuracy. A comparative analysis of Artificial Neural Networks (ANN) and Support Vector Regression (SVR) models is conducted during the calibration of reinforced concrete column components, utilizing 10,000 data sets. The results demonstrate that the ANN model significantly outperforms the SVR model, particularly in handling highly nonlinear problems, showcasing its superior predictive capabilities. The study further explores Bayesian calibration using both models, where the ANN model provides smoother and more reliable posterior distributions and trace plots, indicating its stronger ability for parameter estimation and uncertainty quantification. In contrast, the SVR model presents irregular fluctuations and multi-modal posterior distributions, reflecting difficulties in capturing parameter uncertainties. Additionally, a comparison of computational efficiency reveals that, despite its higher training cost, the ANN model is better suited for large-scale simulations and real-time applications due to its faster inference time. Overall, these findings emphasize the outstanding performance of the ANN model and highlight the potential of AI-enhanced computational methods for improving uncertainty quantification in engineering applications. Future research could explore hybrid models and broaden the application of Bayesian calibration to a wider range of structural conditions.