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An animal's diet breadth is a central aspect of its life history, yet the factors determining why some species have narrow dietary breadths (specialists) and others have broad dietary breadths (generalists) remain poorly understood. This challenge is pronounced in herbivorous insects due to incomplete host plant data across many taxa and regions. Here, we develop and validate machine learning models to predict pollen diet breadth in bees, using a bee phylogeny and occurrence data for 682 bee species native to the United States, aiming to better understand key drivers. We found that pollen specialist bees made an average of 72.9% of their visits to host plants and could be predicted with high accuracy (mean 94%). Our models predicted generalist bee species, which made up a minority of the species in our dataset, with lower accuracy (mean 70%). The models tested on spatially and phylogenetically blocked data revealed that the most informative predictors of diet breadth are plant phylogenetic diversity, bee species' geographic range, and regional abundance. Our findings also confirm that range size is predictive of diet breadth and that both male and female specialist bees mostly visit their host plants. Overall, our results suggest we can use visitation data to predict specialist bee species in regions and for taxonomic groups where diet breadth is unknown, though predicting generalists may be more challenging. These methods can thus enhance our understanding of plant-pollinator interactions, leading to improved conservation outcomes and a better understanding of the pollination services bees provide.