Wildfire susceptibility is a measure of land propensity for the occurrence of wildfires based on terrain's intrinsic characteristics. In the present study, two stochastic approaches (i.e., extreme learning machine and random forest) for wildfire susceptibility mapping are compared versus a well established deterministic method. The same predisposing variables were combined and used as predictors in all models. The Portuguese region of Dão-Lafões was selected as a pilot site since it presents national average values of fire incidence and a high heterogeneity in land cover and slope. Maps representing the susceptibility of the study area to wildfires were finally elaborated. Two measures were used to compare the different methods, namely the location of the pixels with similar standardized susceptibility and total validation burnt area. Results obtained with the stochastic methods are very alike with the deterministic ones, with the advantage of not depending on a priori knowledge of the phenomenon.