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This study aims to develop a hybrid machine learning model, so-called Genetic algorithm–Artificial neural network (GA-ANN), for efficiently predicting the axial load-carrying capacity (ALC) of concrete columns reinforced with fiber reinforced polymer (FRP) bars. For that, a set of 280 experimental test data is collected to develop the GA-ANN model. Seven code-based and empirical-based formulas, which were proposed by various design codes and published studies, are also included in comparison with the developed machine learning model. The performance results of GA-ANN are compared with those of seven previous equations. Statistical properties including goodness of fit (), root mean squared error (), and are calculated to evaluate the accuracy of those predictive models. The comparisons demonstrate that GA-ANN outperforms other models with very high and values (i.e., 0 …