Inter-basin water transfers (IBTs) are important components of water balances of basins, and they can have substantial impact on regional water availability. Flow information is often not available at locations with known IBTs, which is a drawback in several published IBT databases. Few, if any, studies examine whether IBT flow behavior can be generalized, and if these behaviors can be predicted at undocumented locations or known IBT locations with no flow information. In this study, we employ a clustering method based on image matching to identify similar classes of flow behavior of IBTs. Machine learning models are used to assess how well IBT flow characteristics (e.g., average flow) associated with these behaviors can be predicted. These evaluations of IBTs are done for two regions in the United States. Three primary classes of IBTs (seasonal, nonseasonal/not mixed, and seasonal/mixed) are identified across the two regions analyzed. The IBT flow characteristics are accurately predicted in the northeast region. In the Colorado region, however, only the flow characteristics related to timing were accurately predicted. These results indicate that the proposed modeling framework can be used to identify generalizable IBT flow characteristics. This framework is shown to predict flow characteristics with a reasonable amount of accuracy to undocumented locations and improves previously published IBT databases by backfilling flow information to locations with a known IBT presence.