  To address the urgent challenge of climate change, there is a critical need
to transition away from fossil fuels towards sustainable energy systems, with
renewable energy sources playing a pivotal role. However, the inherent
variability of renewable energy, without effective storage solutions, often
leads to imbalances between energy supply and demand. Underground Hydrogen
Storage (UHS) emerges as a promising long-term storage solution to bridge this
gap, yet its widespread implementation is impeded by the high computational
costs associated with high fidelity UHS simulations. This paper introduces UHS
from a data-driven perspective and outlines a roadmap for integrating machine
learning into UHS, thereby facilitating the large-scale deployment of UHS.
