This review explores recent advancements in hydrogen storage, focusing on sorbent materials and machine learning. Hydrogen, a promising clean energy carrier, faces challenges in storage due to its low volumetric energy density. Current storage methods, such as compressed gas and cryogenic liquid, are inefficient and unsafe. Solid-state storage, including metal hydrides, complex hydrides, and carbon-based materials, offers a safer and more compact solution. Sorbent materials like metal–organic frameworks (MOFs), covalent organic frameworks (COFs), porous carbon-based adsorbents, and high-entropy alloys are being investigated for their potential to enhance hydrogen storage efficiency. MOFs, with their high surface areas and tunable pore sizes, show exceptional hydrogen storage capacities, reaching up to 9.2 wt.% at cryogenic temperatures. COFs also demonstrate high surface areas and promising hydrogen storage capacities, with some achieving up to 6.8 wt.% at 77 K. Machine learning is playing a crucial role in predicting efficient storage materials and optimizing their properties. The review highlights the potential of these materials and technologies to overcome current storage limitations, paving the way for more efficient and sustainable hydrogen storage systems.This review explores recent advancements in hydrogen storage, focusing on sorbent materials and machine learning. Hydrogen, a promising clean energy carrier, faces challenges in storage due to its low volumetric energy density. Current storage methods, such as compressed gas and cryogenic liquid, are inefficient and unsafe. Solid-state storage, including metal hydrides, complex hydrides, and carbon-based materials, offers a safer and more compact solution. Sorbent materials like metal–organic frameworks (MOFs), covalent organic frameworks (COFs), porous carbon-based adsorbents, and high-entropy alloys are being investigated for their potential to enhance hydrogen storage efficiency. MOFs, with their high surface areas and tunable pore sizes, show exceptional hydrogen storage capacities, reaching up to 9.2 wt.% at cryogenic temperatures. COFs also demonstrate high surface areas and promising hydrogen storage capacities, with some achieving up to 6.8 wt.% at 77 K. Machine learning is playing a crucial role in predicting efficient storage materials and optimizing their properties. The review highlights the potential of these materials and technologies to overcome current storage limitations, paving the way for more efficient and sustainable hydrogen storage systems.