Much of our current understanding of nanoconfined electrolytes has come from molecular simulations. However, these studies have been almost exclusively carried out using classical force fields. As these models were parameterized to reproduce the properties of bulk electrolytes, it is unclear whether such a force field can faithfully capture changes in electrolyte structure in the limit of an ultra-confined, quasi-two-dimensional electrolyte. Notably, force field-based predictions of a given confined electrolyte's properties vary substantially depending on the parameters used, with no consensus on the extent of ion adsorption, the distance between adsorbed ions and confining wall, and the relative prevalence of cations and anions at the interface. Although first-principles approaches such as ab initio molecular dynamics (AIMD) can provide greater accuracy, these simulations are too computationally costly to adequately sample the complex configuration space of a nanoconfined electrolyte system. AIMD of confined electrolytes has thus far been limited to very short simulations on small systems, in some cases with only a single ion. Machine learning-based interatomic potentials provide a promising means of overcoming the limitations of conventional simulation methodologies for confined electrolytes. In this approach, we train a machine learning model to reproduce the potential energy surface of a first-principles reference method such as density functional theory (DFT). Such potentials allow for simulations with the accuracy of AIMD but at orders of magnitude lower cost, allowing us to access the long time and length scales necessary for characterizing a nanoconfined electrolyte. While machine learning potentials have been successfully developed for bulk electrolyte solutions and nanoconfined water, to date we are unaware of such a model for a nanoconfined electrolyte, where simultaneously capturing interactions between the ions, water, and confining material adds considerable complexity.