In order to predict the behavior of soil-related phenomena, it is necessary to have knowledge about unsaturated flow and using models that provide optimal estimates of the retention curve and hydraulic conductivity of soils. Despite the widespread use of the classic van Genuchten-Mualem model (VGM), this model usually performs poorly in predicting hydraulic conductivity and modification of some of its parameters seems necessary. In this research, a number of 283 soils from different texture of the UNSODA bank were selected and divided into two sections of calibration and validation sections and their soil parameters were extracted and categorized. Then, by defining the modified unsaturated hydraulic conductivity (Ksc) instead of the saturated hydraulic conductivity (Ks) and determining the limits for l and n parameters, the hydraulic conductivity-moisture function of this model were solved using 24600 pairs of points li and nj for each soil of the three main soil texture classes. In the following, the optimal l value (l̂) of each texture class was selected based on the minimum value of the hydraulic conductivity estimation error using the root mean square error (RMSE) index and the n values that create the minimum errors were selected as the optimal pore size distribution coefficients of the hydraulic conductivity-moisture function (n̂opt). In order to create pedotransfer functions for estimating n̂opt, we ran stepwise regression in MATLAB software considering the condition of statistical significance (P-value=0.05) for independent variables and functions for each soil texture class. After creating pedotransfer functions, the results of the proposed method of this research (MVGM) were compared with the VGM model results using RMSE and Nash-Sutcliffe (NSE) indices. The results showed that in both sections of creating and validation functions, the MVGM performed better in estimating hydraulic conductivity and had a higher efficiency index for all classes of soil texture.