Accepted: 10 June 2020 Runoff estimation in a watershed is very important for efficient management of scarce water resources. Soil information is essential information for runoff estimation. Data collecting and determination of soil textural classification for large territory using the traditional method, ie laboratory testing is time-consuming and costly. Therefore, this study suggested a model based on the combination of Radial Basis Neural Network (RBNN) model, Geographic Information System (GIS), Remote Sensing (RS) and field data to create a digital soil map. This model was studied as a case study in western Iraq, and it was tested using performance parameters. The findings of this model were further confirmed using the hydrological soil group developed by the United States Geological Survey (USGS). The adopted model has been successful in predicting the spatial distribution of clay soil, followed by both silt and sand. It was also noted that the Root Mean Square Error (RMSE) for clay, silt and sand is 4.2 percent, 9.5 percent and 11.0 percent respectively, while the highest value was for the coefficient of clay soil correlation (0.749). Furthermore, there are only four samples out of 25 that have minor variations in the estimated and measured soil texture category defined by USGS. The methodology adopted in this study is therefore well practical for soil classification. Additionally, a broad scale will produce high-quality runoff measurement.