The present study aimed to investigate the possibility of using artificial neural network to estimate the spatial distribution, preparation of predictive distribution of plant species and recognizing the strengths and weaknesses of this method. For this purpose, sampling of vegetation and environmental factors was carried out after determining homogeneous units using digital elevation model and of Geological map scale of 1: 25,000. Then, environmental variables maps were prepared using GIS and geostatistics. The input variables of the neural network were selected based on the results of logistic regression. Predictive distribution modeling was done by Multilayer Perceptron network. Simulation of the presence and absence probability was done after determining the optimal structure of the neural network using mean square error, then, continuous map of the presence or absence probability of species in each habitat was developed using Arc GIS software and optimal threshold was determined. Assessment of the predictive and actual map agreement  by calculation of kappa coefficient showed that predictive map of Seidlitzia rosmarinus has excellent and Cornulaca monacantha has very good correspondence, predictive maps of the  S.orientalis - Artemisia sieberi ، S. orientalis-Astragalus albispinus، A. sieberi₁، A. sieberi₂  ,  R.ribes-A. seiberi predictive maps of habitat has moderate agreement and predictive maps of the A.sieberi-S. orientalis  and A. sieberi- Z. eurypterum has the poor agreement with actual maps of these species.