Agriculture is the main non-point polluter of groundwater in irrigated areas as fertilizers and other agrochemicals are the main contaminants in the water that drains out of the root zone to recharge the aquifer. Nitrates from fertilizers, dissolved in percolation losses from fields. The concentration of nitrates in the percolated water depends on the distributed field water and nitrogen balances over the area. Its concentration in the groundwater depends on the total recharge, pollution loading, groundwater flow and solute traort within the aquifer. In this study, application of intelligent techniques such as Neural networks (ANN), Support vector machines (SVR) and multiple regression for contaminant traort modeling is evaluated and the results compared with Modflow models and geostatistical simulations. For case study select the Arak plain that placed in central of Markazi province of Iran and distribution of nitrate in groundwater in this plain is investigated. We have the result of sampling of 40 selected wells for 4 different seasons from autumn 1385 to summer 1386 in the amount of nitrate, pH, electrical conductivity, total dissolved solids, heavy metals, iron, total coliform, BOD and COD. In summer 1391 duo to validation of the models, sampling of the wells is repeated and accuracy of the modeling results is evaluated. Also, due to the need to identify and determine the type of aquifer and bedrock depth, Geophysical Studies have been done and 42 geoelectric sounding designed and performed. With using neural networks, support vector machines and multivariate regression models, nitrate concentrations in the aquifer as a function of input variables was obtained. Results shows that the estimates of neural networks method with more than 77% accuracy in compared with 48% and 65% correlation between observation and calculation values in multiple regression method and SVR method, has better alignment And this model can be used to estimate the concentration of nitrate in Arak aquifer. In addition performing geostatistical simulation method (SGS), estimated the spatial distribution of nitrate in aquifer. Modeling and prediction results shows that Nitrate concentrations in seasonal rainfall expanding from local to regional areas zone of the aquifer. Also plume of contamination moved to the East and South of the plain. R esult of samples taken in the plain in the summer of 1391 confirmed the accuracy of the modeling results and shows the high concentration of nitrate and other pollutants such as TDS and EC that may reveal the need for greater attention to the pollution of the aquifer.