Radioactive wastes, including used nuclear fuel and decommissioning wastes, have been treated using molten salts. Electrochemical sensors are one of the options for in-situ process monitoring using molten salts. However, in order to use electrochemical sensors in molten salt, the surface area must be known. This is because the surface area affects the current of the electrode. Previous studies have used a variety of methods to determine the electrode surface area in molten salts. One method of calculating the electrode surface area is to use the reduction current peak difference between electrodes with known length differences. The method is based on the reduction peak and has the benefit of providing long-term in-situ monitoring of surfaces immersed in molten salt. A number of assumptions have been made regarding this method, including that there is no mass transport by migration or convection; the reaction is reversible and limited by diffusion; the chemical activity of the deposit should be unity; and species should follow linear diffusion. For the purpose of overcoming these limitations, a variety of machine learning algorithms were applied to different voltammogram datasets in order to calculate the surface area. Voltammogram datasets were collected from multiarray electrodes, comprising a multiarray holder, two tungsten rods (1 mm diameter) working electrodes, a quasi-reference electrode, and a counter electrode. The multiarray electrode holder was connected to the auto vertical translator, which uses a servo motor, for changing the height of the rod in the molten salts. To make big and diverse data for training machine learning models, various concentrations of corrosion products (Cr, Fe) and fission products (Eu, Sm) in NaCl-MgCl2 eutectic salts were used as electrolyte; electrolyte temperatures were 500, 525, 550, 575, and 600°C. This study will demonstrate the potential of utilizing machine learning based electrochemical in situ monitoring in molten salt processing.