Herein, we investigate ion transport in a set of prototypical nanoconfined electrolytes with first principles-level accuracy using a neural network potential developed and validated in our previous work. Machine learning-based interatomic potentials such as this offer a means of retaining the high accuracy of AIMD but at orders of magnitude lower cost, allowing us to perform the long-time, large-scale simulations necessary for characterizing transport. We quantify how the electrolyte conductivity varies as a function of the degree of confinement, then establish how this trend arises based on changes in electrolyte structure, ion-ion correlations, and ion diffusion mechanisms. We envision that the mechanistic understanding of confined ion transport developed in this work will contribute towards the development of nanoporous materials with high conductivity and ion selectivity for applications ranging from energy storage to filtration.