Abstarct: According to the human requirement such as mineral supplement, transportation, underground storage etc., tunnel construction is an evident need. Overbreak is one of the unfortunate phenomenons that we are encountering in the tunneling, particularly in the drill and blast tunneling method. Overbreak phenomenon, cause to decreasing the safety of working environment and increasing the operational costs.Overbreak prediction is the first step to decreasing the damaging effects of this phenomenon in the tunnel construction process. Causing factors of over break are classified into two groups of uncontrollable factors(geological parameters) and controllable factors(blasting parameters) and all of the factors are nonlinearly correlated. In this study, at first, 262 sets of causing factors and overbreak data were applied to the multiple linear and nonlinear regression, Artificial Neural Network, Fuzzy logic, Adaptive Network-baxsed Fuzzy Inference System and support vector machine to predict over break as input and output parameters, respectively. The result of prediction models illustrate that the fuzzy and adaptive network-baxsed fuzzy inference system models, have done more appropriate prediction than other prediction models. With awareness of the overbreak occurrence, we can use controlling and preventing methods to reduce the harmful effects of this phenomenon, and ultimately improve project performance.