<p>Accurately predicting the maximum load-carrying capacity of Fiber Reinforced Polymer Reinforced Concrete (FRP RC) columns is a formidable challenge due to the controversial contribution of FRP bars under compression. This study employs six machine learning (ML) techniques—Multiple Linear Regression, Gaussian Process Regression, K-Nearest Neighbor, Support Vector Machine, Artificial Neural Network, and Random Forest Regression (RFR)—to scrutinize various variables' effects on FRP RC columns. Leveraging a comprehensive dataset of 318 columns from diverse literature sources, the study provides nuanced insights into intricate relationships among variables affecting FRP RC columns. Among the ML models, RFR stands out as the best-performing, demonstrating superior performance for both training and testing datasets. The study reveals improved prediction accuracy compared to existing design equations, holding practical implications for structural engineers by offering more reliable predictions for FRP RC column load-carrying capacity. To further illuminate the predictive model, Feature Importance and Partial Dependence Plots were generated using the best-performing RFR model. These plots illustrate the impact of influential parameters on the maximum load-carrying capacity of FRP RC columns. This additional analysis enhances the interpretability of the model, providing engineers with valuable insights into the importance of each parameter. In conclusion, this study not only advances the understanding of FRP RC column behavior and presents practical tools in the form of machine learning models but also facilitates a deeper exploration of the model's insights through Feature Importance and Partial Dependence Plots. The findings, especially with the use of RFR, hold promise for improving the accuracy and interpretability of predictions in structural engineering and design practices, contributing to safer and more effective structural designs.</p>