Cancer is one of the leading causes of mortality in the world with 9.6 million deaths recorded globally for the year 2018 alone. It involves uncontrolled cell division due to the activation of carcinogen genes and causes disorders in the growth of the tissue, which can occur in any part of the human body. Oral cancer (OC) is one of the prominent cancer types, especially in India, where 11.54% of new cases and 10.16% of deaths are caused by OC. To date, there is no promising treatment to cure cancer. Early detection of cancer can increase the chances of survival and quality of life after the treatment. Nowadays, various imaging and non-imaging diagnosis techniques are available. Imaging techniques became popular due to their non-invasiveness, nonpainful nature, and repetitiveness. X-ray, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and fluorescence imaging are some of those techniques. Fluorescence imaging uses fluorescence contrast agents, whereas all other techniques use ionizing radiation, which is harmful when repetitive imaging is required. However, all these techniques have their pros and cons. Recently, the research community has been working on thermal imaging, photoacoustic imaging, and hyperspectral imaging (HSI) to overcome such limitations. HSI is a promising technique for in vivo diagnosis, due to its multi-band capturing capability. It can capture the same location tissue with a higher spatial and spectral resolution, for a wide range of wavelengths from visible to near-infrared (NIR). It provides an ionization-free diagnosis, is less dependent on skilled pathologists, and produces quick results, and it is even safe for one to undergo this procedure many times. HSI can also be used for the effective identification of resection margin while operating to remove the OC tumor. It normally generates a huge three-dimensional data cube, where the effective processing of these data can produce good results. Currently, the research community is working on the OC HIS data using deep learning techniques like CNN, 3DCNN, R-CNN, Mask R-CNN, Customized CNN, etc. In this chapter, we present state-of-the-art works employing HSI with deep learning techniques for the early detection of OC and propose future research directions to the OC research community.