Recent studies have demonstrated that the inferior temporal (IT) cortex of macaques encodes various attributes of visual objects, including identity, size, position, and pose (Hong et al., 2016). However, the representation of object depth within IT has remained relatively unexplored. In this study, we investigate whether monocular depth information can be decoded from the distributed population activity across the macaque IT cortex and artificial neural network (ANN) models of the macaque ventral stream. Using 465 single-object images from the Microsoft COCO dataset, annotated with depth information across ten object categories, we found that ANN models of IT (VGG-16) significantly predict object depth (R = .77, p < 0.001). Partial correlation analysis, controlling for size (R = .69, p < 0.001), confirmed that depth is independently represented. To investigate whether biological systems show similar patterns, we recorded neural activity from 576 sites in two macaques’ IT cortex as they passively viewed the same images (100 ms presentations). We successfully decoded depth information from the neural responses, comparable to ANN models (R = .71, p < 0.001). Interestingly, we also observed that size and depth are entangled together in the ANN representation during earlier layers and get increasingly disentangled with depth (as object identity becomes more linearly separable). We reasoned that such phenomena might be evident in the temporal evolution of the macaque IT responses. Indeed, shortly after image onset, size and depth representations are entangled. Over time, as the linear separability of object identity improves, these attributes become increasingly disentangled, suggesting a progressive refinement of depth representation in IT---a temporal property not yet implemented in current ANN models. These findings reveal depth as an explicit attribute represented in the macaque ventral stream and suggest that the temporal dynamics of attribute disentanglement could inform improvements in ANN models to better mimic biological processing.