ABSTRACT Passive monitoring based on audio or camera recordings is a valuable tool for wildlife conservation and management. The Hainan gibbon ( Nomascus hainanus ), with a global population of approximately 42 individuals, is one of the world's rarest mammals, and establishing effective monitoring approaches to understand the species' landscape use and population dynamics is a conservation priority. Hainan gibbons live in small social groups that share limited vocal characteristics. Although previous research has identified both species‐ and individual‐specific vocal signatures in gibbon calls, no studies have examined the identification of social groups from their vocalizations. We assessed the possibility of identifying gibbon social groups from their calls using deep learning, analyzing 98 8‐h recordings from three groups collected between 2015 and 2016. In a closed population with known groups, the best models recognized the calling gibbon groups with 95.24% average accuracy. When matching pairs of calls without knowledge of all groups, models identified pairs of calls with 90.70% average accuracy, falling to 64.89% for calls from unseen groups during training. Training on call sequences improved identification accuracy more than training on individual segments. Pooling votes from segment‐based predictions helped but was less effective than full sequence models. Deep learning outperformed random forests, especially on sequence‐based tasks. Analysis of confounding factors showed that gibbon acoustic signatures, not background noise, primarily influenced identification accuracy. These findings highlight the possibility of identifying gibbon social groups from their calls and the potential of deep learning to enhance acoustic passive monitoring efforts.