<p>The sustainable use of nuclear fuel and the cleanup of contaminated water sources necessitate the efficient recovery of uranium from aqueous sources. Conventional techniques for uranium recovery exhibit poor selectivity, expensive processes, and inadequate performance in complex ionic solutions such as seawater and nuclear waste with the uranium concentration of (~ 3.3ppb) and (~ 10ppm) respectively. Hydrogel adsorbents based on Graphene oxide (GO), due to their high surface area, good stability, porous three-dimensional structures, and tuneable surface chemistry, have emerged as a new class of adsorbents for uranium recovery in recent years. This review offers a comprehensive and critical review of graphene oxide-based hydrogels for the extraction of uranium (VI) from aqueous environments. A discussion of the underlying chemistry of uranium speciation, adsorption, and hydrogel synthesis identifies the structure property relationships that shows uranium capture in hydrogels. The adsorption capacity, selectivity, kinetics, and regeneration of several functionalized Graphene oxide (GO) hydrogel systems are reviewed, including reduced Graphene oxide (rGO), amidoxime functionalized, phosphoryl functionalized, and multi ligand-based systems. Substantial developments have been made, but challenges remain in the long-term stability, scalability, antifouling properties, and practical deployment of these systems. By critically comparing various functionalization schemes and identifying knowledge gaps, this review aims to map a course for future research on multi-ligand system GO based hydrogels and integrate machine learning (ML) approaches to predict adsorbent design, prioritize functionalization strategies, and reducing experimental trial-and-error. This paper offers a data-driven methodology for the logical design of next-generation GO adsorbents for uranium removal, providing useful information for nuclear wastewater treatment and environmental remediation.</p> Graphical Abstract <p></p>