Soil bulk density is an important soil parameter directly related to a number of soil properties and processes and required to estimate element stocks in soils on an area basis. The measure of ρb is expensive and time-consuming and thus is often excluded from ordinary analyses. It is thus necessary the development of proper pedotransfer functions (PTF) to estimate ρb from parameters ordinarily included in soil analyses. In this study we used a geochemical database of 115 epipedons from Galicia (NW Spain) to test 3 different statistical methods – multiple linear regression, random forest and neural networks – in order to develop a PTF linking bulk density to organic matter content and soil textural fractions. Random forest was the model that presented the highest predictive performance (R-squared=0.90; RMSE=0.14; ME=0.03). This PTF was used to generalize a map of ρb covering the study area. Soil bulk density in Galicia is mainly related to the soil carbon content, peat soils being the features with lower ρb in this study area.