Detailed information on the geographic distribution of a crop is important in planning efficient germplasm conservation strategies but is often not available, particularly for minor crops. Using germplasm collection data from Kenya, Tanzania, and Uganda, we used distribution modeling to predict the distribution of sweetpotato [Ipomoea batatas L. (Lam.)] in sub-Saharan Africa. We used a consensus modeling approach using the following algorithms: genetic algorithm for rule set prediction (GARP), maximum entropy, BIOCLIM, and DOMAIN. The predicted distribution encompasses known sweetpotato production areas as well as additional areas suited for this crop species. New geographic areas where at least three models predicted presence were in Angola, Cameroon, Central African Republic, The Congo, Democratic Republic of Congo, Gabon, Ghana, Angola, Ethiopia, Mozambique, Rwanda, and the Central African Republic. This information can be used to fill gaps in current sweetpotato germplasm collections as well as to further enhance the current presence-only based distribution model. Our approach demonstrates the usefulness of considering several models in developing distribution maps.