It is difficult to consider the prediction of ground settlement under the coupling effect of multiple factors for the finite element method and formation loss rate. Based on the multi-factor and nonlinear fitting ability of back-propagation neural network（BPNN） and random forest（RF），these two machine learning algorithms are adopted to predict the tunneling-induced ground settlement. The optimum hyper-parameters of the two machine learning algorithms are determined by particle swarm optimization（PSO），and k-fold cross validation method is used to improve the robustness of the prediction method. The prediction results indicate that the prediction error of BP neural network is larger and it’s hard for BP neural network to predict the large settlement. The random forest algorithm can accurately predict the maximum settlement and longitudinal ground settlement curve.