Intensity mapping (IM) charts the large-scale structure of our Universe across epochs, including the Epoch of Reionization, by tracing emission lines, from the rest-frame UV and optical, over (sub)mm, to the radio. Beyond the ‘tip of the iceberg’ of bright sources, this technique integrates line emission at comparably low resolution, to also catch the light from the bulk of faint sources and diffuse emission. IM is able to constrain structure growth, the state of the IGM as well as properties and environment of ionising sources. Most prominently, the Square Kilometre Array (SKA) saw its first light in 2024 and is setting sail to revolutionise our understanding of galaxy evolution and cosmology by mapping >50% of the observable Universe via the 21cm radio line of neutral hydrogen. As an IM researcher faced with a complex signal (highly non-Gaussian, foreground-contaminated) and large data volumes (raw rates of up to TB/s) naturally the question of setting to work modern machine learning (ML) arises. In this talk I give an overview of line intensity mapping at large and findings from SKA precursor instruments, to then focus on the SKAO and its capabilities, current efforts and longer-term goals. To reach these goals, modern ML already plays an integral part for tasks such as detection, denoising and inference. My group in particular is researching optimal inference and generative methods for line tomography to learn about galaxy evolution and IGM properties, with emphasis on generalisability and robustness to noise and systematics. I close by a broader look at how modern ML transforms the analysis of astronomical surveys beyond IM, such as on-the-fly data-driven classification for 4MOST (the new ESO workhorse spectrograph), rendering interdisciplinary research and a community-wide exchange on best methods and practices the more important. Those unable to attend the colloquium in person are invited to participate online through Zoom (Meeting ID: 942 0262 2849, passcode 792771) using the link: https://eu02web.zoom-x.de/j/94202622849?pwd=dGlPQXBiUytzY1M2UE5oUDRhbzNOZz09