Abstract: Objective To investigate and analyze the spatiotemporal distribution of Oncomelania hupensi in Jinshan District of Shanghai City from 2006 to 2025, so as to provide an assessment reference for risk control of the intermediate host of Schistosoma japonicum (Oncomelania hupensi). Methods O. hupensis surveillance data in Jinshan District of Shanghai City from 2006 to 2025 were collected at the administrative village scale. The data were organized using Excel 2021 software, spatial autocorrelation analyses were conducted with ArcGIS 10.8 software, and spatiotemporal clustering analyses were performed using SatScan 10.1.3 software. Results From 2006 to 2025, a total of 163 snail habitats were discovered in 30 administrative villages in 6 towns (High-tech Zone) in Jinshan District of Shanghai City, including 135 newly detected snail habitats (82.82%) and 28 recurrent snail habitats (17.18%). A total of 101265 m² of snail-present areas were identified, with 2673 live snail frames surveyed and 20987 snails captured. The highest density reached 200 snails per 0.11 m² (in 2021), with an average live snail density of 0.47 snails per 0.11 m² and an average live snail frame occurrence rate of 5.98%. A total of 16584 snails were dissected, with no positive snails detected. Fengjing Town had the highest number of snail villages (11) and snail habitats (76), while Langxia Town had the largest snail area (73450 m²). The global spatial autocorrelation and cluster and outlier analyses showed that there was spatial clustering in the snail habitats for the entire district, (Moran's I = 0.109 ~ 0.167, all P < 0.01), and there were 4 high-high (HH) cluster points (where both the local village and neighboring administrative villages had a high number of snail sites), 1 high-low (HL) cluster point (where the local village had a high number of snail sites but the neighboring administrative villages had fewer), 7 low-high (LH) cluster points (where the local village had a low number of snail sites but the neighboring administrative villages had a high number), and no low-low (LL) cluster points (where both the local village and neighboring administrative villages had a low number of snail sites). The spatiotemporal scanning results revealed a single primary clustering area, involving 5 administrative villages in Fengjing Town, which were predominantly located along the border areas with other districts（ Qingpu District and Songjiang District） and provinces (Zhejiang Province). The clustering period occurred from 2011 to 2012. Conclusion From 2006 to 2025, the distribution of O. hupensis in Jinshan District of Shanghai City exhibited pronounced spatiotemporal clustering. No infectious Oncomelania snails were detected in this district, yet their remnants persisted, with a relatively high proportion of newly identified snail habitats. Inter-provincial and inter-district borders constituted high-risk areas for Oncomelania snails. It is recommended to strengthen interprovincial and interregional joint prevention and control measures in primary and secondary clustering areas, improve the effectiveness of snail inspection and eradication, and strengthen the control of Oncomelania snails in high-risk areas in a manner tailored to local conditions.