With the development of reinforced concrete bridges, the fatigue problem of concrete has attracted extensive attention. Failure occurs when concrete is subjected to cyclic compression that is less than the static compressive strength of concrete after a certain amount of cyclic loading (fatigue life). Compared to traditional methods with shortage of low accuracy, resulting uncertain safety problem, to address this issue, we utilized gradient boosting regression tree (GBRT) and multiple linear regression (MLR) to predict the fatigue life of concrete under repeated compression based on the collated 275 sets of experimental data. All prediction results were compared with that calculated by the existing European codes. Results analysis reveal that both proposed GBRT and MLR models have lower errors and higher correlation coefficients than that of existing formula/code models. Furthermore, the prediction accuracy of the GBRT model can be improved by using the existing classical formulas to reconstruct the features and selecting suitable features by ranking the feature importance, without increasing the number of features. Experimental results reveal that GBRT model can achieve the best performance with lowest RMSE and highest R 2 . Moreover, the sensitivity analysis revealed that the proposed GBRT model is insensitive to input characteristics of fatigue life, which can be used for accurate calculation and design analysis of concrete compressive fatigue life, providing important reference value for formulating concrete technical design specification based on fatigue life.