A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R2 of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR. Display omitted •Six XGBoost-based hybrid models for predicting TBM penetration rate are proposed.•GWO, PSO, SSO, SCA, MVO and MFO can assist the hyper-parameters tuning of XGBoost.•The prediction performance from high to low is PSO-XGB, MFO-XGB, GWO-XGB, MVO-XGB, SCA-XGB, SSO-XGB.•The Mutual information method is applied to demonstrate the relative importance of each input indicator.