The main purpose of this study is to investigate the use of hierarchical fuzzy inference systems (HFISs) in expert-based landslide susceptibility mapping in a data-scarce region. Taounate-Ain Aicha and Tahar Souk regions in the central part of the Rif Mountains in Morocco were selected as the case study area. The research was performed in three main stages: (i) the landslide inventory of the region was produced and the conditioning factors were evaluated; (ii) the theoretical background for HFIS was introduced; and (iii) different types of structures and methods of HFIS were investigated in the construction of expert-based models; the landslide information was only used for validation of the expert-based models in this stage. Regarding the inference methods, the defuzzification-free hierarchical fuzzy system (DF-HFS) has not only the remarkable advantage of low cost of computation but also preservation of information. The most successful result was acquired from the model developed using HFISs, which was designed by considering a defuzzification-free hybrid structure with standard membership functions. This research is the first study in which HFISs are evaluated in expert-based landslide susceptibility mapping. HFISs and the rule-generation algorithm implemented in this study will allow fuzzy systems to be applied effectively not only in landslide susceptibility mapping but also in other geoscientific and geo-engineering solutions.