Geomorphomety is the quantitative measurement of landforms based on height changes influenced by distance function. Geomorphomety indices express the characteristics of the terrain quantitatively. The present study emphasizes the use of geo-morphometric parameters and SVM algorithm to identify areas prone to landslide in Khorramabad- Paul Zal freeway as one of the important roads in the country. Other indices include slope, aspect, lithology, fault condition, drainage and land use which are applied together with the geometric indices such as profile curvature, plan curvature and total curvature use, artificial intelligence approach and linear functions and polynomial SVM algorithm to identify areas prone to landslides. The results show using Geomorphometric indices play an important role in increasing accuracy of assessing and identifying areas with an increasing the risk of landslides and assessing the accuracy through using land survey data shows that polynomial functions are more accurate in identifying areas prone to landslides than SVM algorithm linear function.