The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes is investigated using machine learning-based molecular dynamics simulations. The study focuses on aqueous NaCl confined within graphene slit pores, revealing that ion pairing behavior in highly confined electrolytes differs significantly from that in bulk solutions. The free energy of ion pairing decreases in contact ion pairs but increases in solvent-separated ion pairs due to the interplay of ion solvation effects and the electronic structure of graphene. These findings are not reproduced by classical force fields, highlighting the importance of first-principles-level simulations for understanding nanoconfined electrolytes. Aqueous electrolytes confined to nanoscale pores are crucial for various technologies, including energy storage and separation processes. Ion-ion interactions in these systems significantly affect the performance of devices such as supercapacitors and membranes for ion-selective separations. Previous studies suggest that ion pairing in confined electrolytes can differ from that in bulk solutions, with variations in ion pairing depending on the degree of confinement and the nature of ion-pore interactions. Molecular simulations have been used to study nanoconfined electrolytes, but classical force fields have limitations in capturing the complex behavior of these systems. Machine learning-based interatomic potentials offer a promising solution, allowing simulations with the accuracy of ab initio molecular dynamics (AIMD) but at a much lower computational cost. This approach enables the study of long time and length scales necessary for characterizing nanoconfined electrolytes. The study presents a first-principles quality machine learning potential to investigate ion pairing in a prototypical nanoconfined electrolyte. The results show that the free energy of ion pairing changes under confinement, with significant differences observed in the most confined system. The behavior is attributed to steric constraints on ion solvation and the electronic structure of the confining surface. The study also highlights the importance of graphene's lattice structure and electronic properties in determining ion pairing behavior. The findings demonstrate that the electronic structure of the confining material plays a crucial role in ion pairing, with graphene's polarizability affecting the electric field and ion pairing behavior. The study provides insights into the role of solvation effects and the confining wall's electronic structure in determining the structure of nanoconfined electrolyte solutions, which may be useful for developing pore materials and geometries that minimize ion pairing, thus improving ionic conductivity and selectivity in energy storage and separation technologies.The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes is investigated using machine learning-based molecular dynamics simulations. The study focuses on aqueous NaCl confined within graphene slit pores, revealing that ion pairing behavior in highly confined electrolytes differs significantly from that in bulk solutions. The free energy of ion pairing decreases in contact ion pairs but increases in solvent-separated ion pairs due to the interplay of ion solvation effects and the electronic structure of graphene. These findings are not reproduced by classical force fields, highlighting the importance of first-principles-level simulations for understanding nanoconfined electrolytes. Aqueous electrolytes confined to nanoscale pores are crucial for various technologies, including energy storage and separation processes. Ion-ion interactions in these systems significantly affect the performance of devices such as supercapacitors and membranes for ion-selective separations. Previous studies suggest that ion pairing in confined electrolytes can differ from that in bulk solutions, with variations in ion pairing depending on the degree of confinement and the nature of ion-pore interactions. Molecular simulations have been used to study nanoconfined electrolytes, but classical force fields have limitations in capturing the complex behavior of these systems. Machine learning-based interatomic potentials offer a promising solution, allowing simulations with the accuracy of ab initio molecular dynamics (AIMD) but at a much lower computational cost. This approach enables the study of long time and length scales necessary for characterizing nanoconfined electrolytes. The study presents a first-principles quality machine learning potential to investigate ion pairing in a prototypical nanoconfined electrolyte. The results show that the free energy of ion pairing changes under confinement, with significant differences observed in the most confined system. The behavior is attributed to steric constraints on ion solvation and the electronic structure of the confining surface. The study also highlights the importance of graphene's lattice structure and electronic properties in determining ion pairing behavior. The findings demonstrate that the electronic structure of the confining material plays a crucial role in ion pairing, with graphene's polarizability affecting the electric field and ion pairing behavior. The study provides insights into the role of solvation effects and the confining wall's electronic structure in determining the structure of nanoconfined electrolyte solutions, which may be useful for developing pore materials and geometries that minimize ion pairing, thus improving ionic conductivity and selectivity in energy storage and separation technologies.