Combination of Adoptive Network based Fuzzy Inference System (ANFIS) and subtractive clustering (SC) has been used for estimation of deformation modulus (E_m) and rock mass strength (UCS_m) considering depth of measurement. To do this, learning of the ANFIS based subtractive clustering (ANFISBSC) was performed firstly on 125 measurements of 9 variables such as rock mass strength (UCS_m), deformation modulus (E_m), depth, spacing, persistence, aperture, intact rock strength (UCS_i), geomechanical rating (RMR) and elastic modulus (E_i). Then, at second phase, testing the trained ANFISBSC structure has been perfomed on 40 data measurements. Therefore, predictive rock mass models have been developed for 2-6 variables where model complexity influences the estimation accuracy. Results of multivariate simulation of rock mass for estimating UCS_m and E_m have shown that accuracy of the ANFISBSC method increases coincident with development of model from 2 variables to 6 variables. According to the results, 3-variable model of ANFISBSC method has general estimation of both UCS_m and E_m corresponding with 20% to 30% error while the results of multivariate analysis are successfully improved by 6-variable model with error of less than 3%. Also, dip of the fitted line on data point of measured and estimated UCS_m and E_m for 6-variable model approaches about 1 respect to 0.94 for 3- variable model. Therefore, it can be concluded that 6-variable model of ANFISBSC gives reasonable prediction of UCS_m and E_m.