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When the effect of two soil erosion features, in this study, gully headcuts (GHs) and pipe collapses (PCs), is investigated jointly, a new comprehensive concept related to their controlling factors can be found. The objective of this paper to evaluate susceptibility to these two features in the hilly region of the Golestan Province (NE Iran), which may be helpful in land management and the sustainable development of the region. The maps of the controlling factors of GHs and PCs were constructed and the random forest algorithm was used to prioritize the factors controlling the occurrence of these two soil erosion features. The GHs and PCs susceptibility maps were prepared using the random forest model in the R software and validated applying the receiver operating characteristic curves, fourfold plot, and Cohen's kappa index, whereas the susceptibility map of the two soil erosion features was prepared by overlapping these maps. The results of factor importance analysis have indicated that land use, slope, and silt content are the most important factors in the occurrence of PCs, whereas slope gradient, silt content, and distance to streams are the most important in GHs occurrence. This means that steep and uncultivated slopes in the study area are most susceptible to GHs and PCs. The susceptibility models of GHs and PCs have excellent accuracy, that is, the area under the receiver operating characteristic curve values of GHs and PCs models were 0.960 and 0.935, respectively. The susceptibility map of GHs and PCs analysed jointly has shown that 66% of the area is not susceptible to any of these soil erosion features, whereas 15% is susceptible to both of them. The places susceptible to the two types of soil erosion features (GHs and PCs) indicate the areas where there is a high probability of GHs retreat due to the PCs. This study has confirmed that it is feasible to forecast the spatial behaviour of GHs and PCs occurrence and development. This is particularly preferred when the developed methods are applied for the susceptibility evaluation under various mitigation scenarios.