<p>Luminescence thermometry affords remote thermal readouts with high spatial resolution in a minimally invasive way. This technology has advanced our understanding of biological mechanisms and physical processes from the macro- to the submicrometric scale. Yet, current approaches only allow obtaining 2D thermal images. This aspect limits the potential of this technology, given the inherent three-dimensional nature of heat diffusion processes. Despite initial attempts, a credible method that allows extracting 3D thermal images via luminescence is missing. Here, we design such a method combining Ag₂S nanothermometers and machine learning algorithms. The approach leverages the distortions in the emission spectra of luminescent nanothermometers caused by changes in temperature and tissue-induced photon extinction. The optimized neural network-based algorithm can extract this information and provide 3D thermal images of complex nanothermometer patterns. Although tested for luminescence thermometry at the in vivo level, this method has far-reaching implications for luminescence-supported 3D sensing in biological systems in general.</p>