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Dust particles are transported globally. They can adversely impact both human health and the environment, but they also impact transportation infrastructure, agriculture, and industry, occasionally severely. The identification of the locations that are the primary sources of dust, especially in arid and semi-arid environments, remains a challenge as these sites are often in remote or data-scarce regions. In this study, a new method that uses state-of-the-art machine-learning algorithms -random forest (RF), support vector machines (SVM), and multivariate adaptive regression splines (MARS) -was evaluated for its capacity to spatially model the distribution of dust-source potential in eastern Iran. To accomplish this, empirically identified dust-source