Prediction of tunneling performance is intimately connected to the prediction of Advance Rate (AR), which is crucial to optimize tunnelling operation and steering tunnelling processes. However, the forecasting AR is accuracy remains a challenge due to its dependency on several factors. Several methods have been introduced to predict the performance of tunnel driving, which are often of two types: closed-form equations or regression analysis. In the study, we developed regression models to predict AR from tunnel boring machine (TBM) based on machine learning (ML) algorithms. Five different popular ML algorithms, including k-nearest neighbor (KNN), support vector regression (SVR) model, artificial neural networks (ANN), random forest regression (RF), and classification and regression tree (DT), were applied to develop TBM AR prediction models using data monitored during construction. More than 600 dataset examples were collected from a TBM project between the Salgueiros and Sao Bento stations of the Porto light metro project in Portugal. 7 useful features were selected based on empiricism. 80% of the dataset were assigned for training and 20% for testing the models. The performance of developed models was evaluated and compared in terms of coefficient of determination (R2) and root mean square error (RMSE). The results show that SVR is the best AR predictor among the five algorithms tested.