Using a small-cell active learning approach, we generate a moment tensor potential (MTP) trained on only 609 configurations, jointly describing solid/liquid Na, gaseous Cl, and crystalline/molten NaCl. This MTP implicitly captures the effect of atomic charge variations on energies and forces based on local atomic configurations. Extensive testing of this potential points to a high-fidelity description of the structural and transport properties of Na and NaCl. Furthermore, this potential was used to calculate the standard reduction potential and solubility limit of Na in molten NaCl. These computed properties are in good agreement with available experimental data and ab initio calculations. Our proposed approach can be utilized to predict the electrochemical and physical properties of molten salts with arbitrary compositions and solutes, as well as the molten salt corrosion of metals.