Given the prowess of deep learning and the unique capabilities of XRD-CT, the combination of these two could potentially revolutionize tomographic image reconstruction. Deep learning methods could not only accelerate the XRD-CT on both data acquisition and analysis but also enhance it by addressing challenges like image super-resolution using high resolution region-of-interest CT scans, data denoising, as well as single-crystal diffraction, self-absorption and parallax artefacts. This combination, if realized, could unlock new possibilities, including higher spatial and temporal resolution in chemical imaging and better handling of complex data sets, paving the way for breakthroughs in various fields.