Digital soil mapping applies soil attributes, Remote sensing and Geomorphometrics indices to estimate soil types and properties at unobserved locations. This study carried out in order to comparison two data mining algorithms such as Random Forest (RF) and Boosting Regression tree (BRT) and two features selection principal component analysis (PCA) and variance inflation factor (VIF) for predicting soil taxonomy class at great group and subgroup levels. A total of 61 soil profile observation based on stratified random determined and digged in area with approximately 16660 hectares.19 RS indices and geomorphometrics covariates derivated from Landsate-8 imagery and DEM with 30 meters’ resolution in ERDAS IMAGINE 2014 and SAGA GIS version 7.0 software’s. Also to run four Data mining algorithms scenarios (PCA-RF, VIF-RF, PCA-BRT, VIF-BRT) from “Randomforest” and “C.5” packages were used in R studio software. 80% and 20% from soil profiles were applied for calibrating and validating. The results showed that in PCA and VIF approaches, eight covariates such as (Relative slope position, diffuse insolation, modified catchment, normalized height, RVI, Standard height, TWI, Valley depth) and six covariates (NDVI, DVI, Catchment area, DEM, Salinity index, Standard height) were selected. The validation results based on overall accuracy and kappa index for scenarios at great group level indicated that 88,93,62, 54 and 75,83,51,45 percentages and for subgroup level had 70, 77, 54, 47 and 60, 71, 43, 37 percentages, respectively. Generally, VIF-RF had accuracy rather than from other scenarios at two categorical level in this study area.