Diabetes is a global public health concern, with India emerging as the largest contributor to this burden. In India, there is an uneven socio-economic and regional distribution of diabetes, but so far it has only been explored at the state level. Thus, this study was done to identify the geo-spatial distribution of elevated blood sugar levels at the district level in India, including determining any macro-level predictors associated with it. A secondary data analysis was conducted using NFHS-5 (2019 - 2021) district-level data, which was freely available in the public domain. This geospatial analysis was performed in four stages. At first district-level spatial distribution map is created, then using Local Indicator of Spatial Autocorrelation (LISA) and Moran's I statistic, statistically significant clusters of districts were identified. Lastly, spatial regression followed by geographically weighted regression (GWR) was performed to identify the local-level predictors of the proportion of individuals with elevated blood sugar levels in a district. Data analysis was done using QGIS v 3.36, GeoDA v 1.44, and R software v 4.3.2. Two major statistically significant clusters in southern & eastern India and two major cold spots in northern & mid-northwestern India were identified. Spatial error regression and Geographically Weighted Regression (GWR) models demonstrated that each percentage point increase in regional tobacco use was linked to a significant relative expansion in district-level elevated blood sugar prevalence, mainly in the eastern hotspot. Similarly, alcohol use showed proportional increases within the southern hotspot. Central obesity in females consistently scaled up prevalence across both clusters. Addressing these regional disparities requires equitable allocation of resources as well as region-wise intervention tailored to dominant risk factors.