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Background: Understanding and mapping the distribution of sandflies and sandfly-associated pathogens (SAPs) is crucial for guiding the surveillance and control effort. However, their distribution and the related risk burden in China remain poorly understood. Methods: We mapped the distribution of sandflies and SAPs using literature data from 1940 to 2022. We also mapped the human visceral leishmaniasis (VL) cases using surveillance data from 2014 to 2018. The ecological drivers of 12 main sandfly species and VL were identified by applying machine learning, and their distribution and risk were predicted in three time periods (2021–2040, 2041–2060, and 2061–2080) under three scenarios of climate and socioeconomic changes. Results: In the mainland of China, a total of 47 sandfly species have been reported, with the main 12 species classified into three clusters according to their ecological niches. Additionally, 6 SAPs have been identified, which include two protozoa, two bacteria, and two viruses. The incidence risk of different VL subtypes was closely associated with the distribution risk of specific vectors. The model predictions also revealed a substantial underestimation of the current sandfly distribution and VL risk. The predicted areas affected by the 12 major species of sandflies and the high-risk areas for VL were found to be 37.9–1121.0% and 136.6% larger, respectively, than the observed range in the areas. The future global changes were projected to decrease the risk of mountain-type zoonotic VL (MT-ZVL), but anthroponotic VL (AVL) and desert-type zoonotic VL (DT-ZVL) could remain stable or slightly increase. Conclusions: Current field observations underestimate the spatial distributions of main sandfly species and VL in China. More active surveillance and field investigations are needed where high risks are predicted, especially in areas where the future risk of VL is projected to remain high or increase. Author summary: Our research provides a comprehensive understanding of the distribution of sandflies and SAPs in China. We have identified 47 sandfly species and 6 SAPs, with 12 main sandfly species forming three distinct ecological clusters. Our machine learning models predict the distribution and risk of sandfly species and VL under various climate and socioeconomic scenarios. The model predictions reveal a significant underestimation of the current sandfly distribution and VL risk. Future global changes are projected to decrease the risk of MT-ZVL, but AVL and DT-ZVL could remain stable or slightly increase. These findings underscore the need for more active surveillance and field investigations of vectors, especially where the future risk of VL is projected to remain high or increase. This study provides important theoretical support for guiding the surveillance and control of sandflies and SAPs. [ABSTRACT FROM AUTHOR]