It was reported that the synergistic effect of process parameters on the superior properties of Inconel 718 exacerbate the wear mechanisms, leading to rapid flank wear rate and unprecedented failure of the PVD-coated carbide inserts during machining. Previous studies employed digital microscopes to identify and measure the dominant wear mechanisms and failure modes as nominal descriptors of flank wear evolution, as well as significant indicators of tool’s remaining useful life and sub-optimal cutting mechanism. However, these methods are time-intensive and involve too much human intervention, which is inefficient for in-process application. Therefore, this study automated the process by leveraging the efficient predictive capability of deep learning architectures to detect the unprecedented failure modes on the tool’s cutting edge as nominal descriptors of rapid flank wear rate, as well as direct indicators of sub-optimal tool performance during machining. To achieve this, an experiment was conducted at various speeds, feeds, and axial depth of cut to characterize the failure modes of PVD-TiAlN/NbN coated carbide inserts during face milling of Inconel 718. The custom imagery dataset, generated from the experimental findings, was pre-processed by labeling the dominant failure modes on the tool’s cutting edge using the MATLAB image labeler application. This created the ground truths for training a custom YOLOv3 tool wear detection model. The trained model was physically validated by conducting another experiment using a new cutting condition to ensure its reliability for real industrial application under unknown cutting conditions. During validation, the model achieved an overall accuracy of 90.86%, with a mean average precision of 0.5659, exhibiting a high efficiency in detecting dominant failure modes, such as notching, built-up edge, flaking, and chipping, on the tool’s flank face.