Abstarct: Soil permeability and cation exchange capacity (CEC) are most important soil parameters but they measurement is difficult and need lots of time as well as costs. With respect to the problem of direct measurement of this parameter in recent year using indirect method such as artificial neural networks has been considered. In the present study, Hundred soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity and infiltration as difficult and time consuming feature. Moreever, soil pH, Porosity and organic matter percentage has measured that after survey sensitivity analysis have delayed. The collected data randomly divided in two categories of training (80%) and testing (20%) and they used for train and test of two artificial neural networks, multi-laxyer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compare with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity and infiltration for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity and infiltration was determined and most important parameters which could influence the soil cation exchange capacity and infiltration were described. Results show that clay and lime percentage with cation exchange capacity and sand percentage and EC with infiltration have most correlation.