Accurate prediction of digital soil maps allows for the evaluation of larger areas with respect to the design of efficient land management plans at the regional scale. Nowadays, there is an increasing demand for high spatial resolution-gridded soil data for crop planning and management because it saves time and costs. One of the most essential soil physical properties affecting water holding capacity, nutrient availability and crop growth is soil texture. It exhibits a high spatial variability, but accurate maps for larger scales are lacking. The aim of this research was to produce gridded maps of soil texture fractions (clay, silt and sand) using regression-based approaches and to establish soil texture classes using classification-based techniques for the semi-arid Piedmont plain of Iran. To this end, a digital elevation model and derived topographic indices, vegetation and soil-based indices generated from 4-year timeseries of remote sensing products of Landsat 8 OLI were used as covariates. The decision tree (linear) and its improved version of random forest (nonlinear) algorithms were used for both the regression and classification analysis. For both algorithms, the topography-based indices and remotely sensed products were the most important predictors for the soil particle fractions. For the estimation of the different textural classes with multiple algorithms, we recorded a moderate overall accuracy rate of 54% and a Kappa coefficient of 17% for the validation datasets. It was observed that the nonlinear classification method of the random forest was more effective, and this was also the case for the regression modelling. In general, the random forest algorithm produced a more useful gridded map to help to design regional management plans based on soil properties.