Debris flow events are frequent in Tajikistan, yet comprehensive investigations at the regional scale are limited. This study integrates remote sensing, Geographic Information System, and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan. A dataset comprising 405 documented debris flow points and 14 influencing factors, encompassing geological, climatic-hydrological, and anthropogenic variables, was established. Three machine learning algorithms-Random Forest, Support Vector Machine (SVM), and Multi-layer Perceptron-were applied to generate susceptibility maps and delineate debris flow risk zones. The results indicate that the areas of higher and high susceptibility accounted for 20.43% and 4.41% of the national area, respectively, and were predominantly concentrated along the Zeravshan and Vakhsh river basins. Among the evaluated models, SVM model demonstrated the highest predictive performance. Beyond conventional topographic and environmental controls, drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan. These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning, and offer practical guidance for selecting conditioning factors in machinelearning-based susceptibility assessments in other dry mountainous environments.