Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6,919 topsoil (0–20 cm) samples in an approximately 7,100 km2 representative area in Denmark. Eighty percent of the data were used for model calibration and the rest for validation. Twelve derivatives extracted from the digital elevation model, together with the information derived from the maps of landscape types, land use, geology, soil types, and georegions, were used as predictors in RT and RKrr modeling. Existing landscape classes were considered for stratification in OKst, and variograms were used to study the spatial autocorrelation. Predicting ability of the models was assessed with R2, RMSE, and residual prediction deviation (RPD) for comparison. Among all the prediction methods, the highest R2 (i.e., 0.74) and lowest RMSE (i.e., 0.28) were associated with the RKrr model, which also had an RPD value of 2.2, confirming RKrr as the best prediction method. Stratification of samples slightly improved the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark.