Control of transboundary diseases at a regional level is commended over the country level due to its inherent complexities. The World Organization for Animal Health (WOAH) has established different zones worldwide to control such contagious diseases as foot-and-mouth disease (FMD). Controlling FMD is difficult because of the complicated connection between FMD risk factors and the deficits of surveillance activities in countries. We used an ecological niche model (ENM) that accounts for the under-reporting of outbreaks to determine FMD risk and risk factors in South Asian countries, India, Bangladesh, and Sri Lanka. Centered on known outbreak information, we predicted high-risk areas using similar regional ecological features. Using a multi-algorithm machine-learning ensemble that includes random forest, support vector, and gradient boosting, 15 predictive variables (i.e livestock densities, land cover, and climate), 660 FMD outbreaks from 13 years (2009-2022) in the region including the outbreaks from India, Bangladesh, and Sri Lanka we identified that Sri Lanka and Bangladesh appeared to have low to medium outbreak risk in the range of 0.04 to 0.55. India was used to fit the model. The machine learning models demonstrated high predictive performance (accuracy>0.87) through cross-validation. Production systems, isothermality, cattle density, and mean diurnal range were identified as the most important predictors of FMD outbreaks. These models help to determine FMD low-risk areas to minimize FMD surveillance activities and high-risk areas to focus on performing additional confirmatory testing, and improve surveillance in a regional context.