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Oscine songbirds learn vocalizations that function in mate attraction and territory defense; sexual selection pressures on these learned songs could thus accelerate speciation. The Eastern and Spotted towhees are recently diverged sister species that now have partially overlapping ranges with evidence of some hybridization. Widespread community-science recordings of these species, including songs within their zone of overlap and from potential hybrids, enable us to investigate whether song differentiation might facilitate their reproductive isolation. Here, we quantify 16 song features to analyze geographic variation in Spotted and Eastern towhee songs and assess species-level differences. We then use several machine learning models to measure how accurately their songs can be classified by species. While no single song feature reliably distinguishes the two species, machine learning models classified songs with relatively high accuracy (random forest: 89.5%, deep learning: 90%, gradient boosting machine: 88%, convolutional neural network: 88%); interestingly, species classification was less accurate in their zone of overlap. Finally, our analysis of the limited publicly available genetic data from each species supports the hypothesis that the species are reproductively isolated. Together, our results suggest that small variations in multiple features may contribute to these sister species’ ability to recognize their species-specific songs.