Based on advanced regional geological information and tunnel settlement data, a long-term settlement prediction model of shield tunnel using machine learning method was proposed. The model was demonstrated by a case study of Nanjing Metro Line 2 shield tunnel. The results show that the prediction model can analyze and obtain the main influencing factors of long-term tunnel settlement and find the best supervised learning algorithm and optimal parameters. Among the different supervised learning algorithms, both the kernel support vector machine (KSVM) algorithm and the artificial neural network (ANN) algorithm can achieve high accuracy, but require careful parameters adjustment to improve the prediction accuracy. Using the ANN algorithm as supervised learning algorithm and after parameters adjusting, the final prediction accuracy of the model can reach 0.86 and the average accuracy of tenfold cross-validation is 0.82.