The study investigates the ion-ion interactions in nanoconfined electrolytes, specifically aqueous NaCl within graphene slit pores, using machine learning-based molecular dynamics simulations with density functional theory (DFT) accuracy. The research reveals that the free energy of ion pairing in highly confined electrolytes deviates significantly from that in bulk solutions, showing a decrease in contact ion pairs (CIPs) and an increase in solvent-separated ion pairs (SSIPs). These changes are attributed to the interplay between ion solvation effects and the electronic structure of graphene. The behavior observed in first-principles-level simulations does not align with classical force field predictions, highlighting the limitations of conventional models in capturing the complex interactions in nanoconfined systems. The study provides insights into predicting and controlling the structure of nanoconfined electrolytes, which is crucial for improving energy storage and separation technologies. The work also demonstrates the effectiveness of machine learning potentials in overcoming the limitations of classical force fields and offers a foundation for exploring a broader range of electrolytes and confining materials.The study investigates the ion-ion interactions in nanoconfined electrolytes, specifically aqueous NaCl within graphene slit pores, using machine learning-based molecular dynamics simulations with density functional theory (DFT) accuracy. The research reveals that the free energy of ion pairing in highly confined electrolytes deviates significantly from that in bulk solutions, showing a decrease in contact ion pairs (CIPs) and an increase in solvent-separated ion pairs (SSIPs). These changes are attributed to the interplay between ion solvation effects and the electronic structure of graphene. The behavior observed in first-principles-level simulations does not align with classical force field predictions, highlighting the limitations of conventional models in capturing the complex interactions in nanoconfined systems. The study provides insights into predicting and controlling the structure of nanoconfined electrolytes, which is crucial for improving energy storage and separation technologies. The work also demonstrates the effectiveness of machine learning potentials in overcoming the limitations of classical force fields and offers a foundation for exploring a broader range of electrolytes and confining materials.