This study aimed to evaluate the Artificial Neural Network (ANN) model potential to predict the spatial distribution of plant species.  The vegetation data as well as topography, climate, geology and soil data were collected. A randomized-systematic method was used to collect the vegetation data. Three parallel transects with 150 meters lengths were established in each sampling unit, and the vegetation data were collected from 15 quadrates placed with intervals of 10 meters. Various geo-statistical methods were used to produce environmental maps, and ANN technique was used to predict the potential vegetation habitats. The produced model was assessed using Kappa coefficient. According to the results, the study area was identified as a good habitat for species such as Agropyron intermedium, Thymus kotschyanus, Astragalus gossypinus and Stipa barbata with Kappa coefficient of 0.95, 0.84, 0.83 and 0.7 respectively. The results indicated that ANN technique has good potential to predict the spatial distribution of range species. According to the results, the accuracy of the model prediction for the species of all four studied habitats was more than 95%. This indicates that the potential habitat maps of plant species can be produced appropriately by using the selected soil and climatic variables in a plant distribution model.