Aqueous electrolytes under hydrophobic graphene confinements exhibit anomalous structural and dynamic properties with important implications in water desalination and energy storage. Although classical force fields and ab initio methods have allowed for considerable progress in qualitatively understanding the cause for such anomalies, the former is often inaccurate due to inadequate treatment of interatomic interactions within confinement, and the latter lacks speed and scalability to large systems. To address these challenges, we employ a machine learning approach to develop a many-body potential that simultaneously achieves ab initio accuracy and high throughput in molecular dynamics simulations. In this work, we compare the structure and dynamics of aqueous NaCl and KCl salts when subjected to varying degrees of graphene confinement. Our findings indicate that K+ exhibits a higher degree of adsorption onto the graphene surface compared to Na+, which is driven by its greater propensity to desolvate and migrate toward the interface. From the dynamics perspective, we observe that water present in aqueous KCl diffuses faster than that in aqueous NaCl due to the higher stability of solvation shells around Na+ than K+. Finally, we underscore the importance of machine learning potentials in accurately predicting the complex behaviors exhibited by such confined systems, which the classical force fields might overlook.