Abstract: A unified moment bearing capacity prediction model is established in order to solve the issues of various models, difficult calculations, and limited accuracy in predicting the moment bearing capacity of reinforced concrete beams strengthened with fiber reinforced polymer (FRP) materials. By collecting the experimental data associated with three typical FRP strengthening types including externally bonded, end anchorage and near-surface mounted FRP from the existing literature, the key factors which affect the bearing capacity of the reinforced beam are determined, and XGBoost (eXtreme Gradient Boosting) algorithm is trained to obtain regressionally the nonlinear relationship between the influencing factors and the moment bearing capacity of the reinforced beam calculated with a unified FRP strengthened reinforced concrete beam moment bearing capacity prediction model. The prediction accuracy of the model is verified through the testing sample set. It is compared with the prediction models based on two representative machine learning algorithms: support vector regression (SVR) and artificial neural network (ANN). The prediction accuracy under different strengthening types is also analyzed. The results illustrate that the R² of the XGBoost-based moment bearing capacity prediction model reaches 0.9417, indicating that the overall accuracy is high. Compared with the prediction model based on SVR and ANN, the R² of the model based on the ensemble learning algorithm XGBoost is increased by 8.00% and 6.70%, the root mean square error is reduced by 33.94% and 30.72%, and the mean absolute error is reduced by 32.38% and 30.51%. It shows that the XGBoost based model has higher accuracy, which is far better than SVR and ANN based model. The R² of the XGBoost based model reaches 0.9472, 0.9631, and 0.9278, respectively under the externally bonded, end anchorage and near-surface mounted strengthening types. It can be seen that the prediction accuracy is relatively good, and the accuracy is at the same level indicating that this model can uniformly considers three different strengthening types. By analyzing the feature importance of the input parameters, the rationality of the model is explained. The research outcome could provide references for the design and application of actual bridge strengthening with FRP.