Stainless steel is known for its superior corrosion resistance in industrial applications. In this work, corrosion modeling of stainless steel 316L is presented using artificial neural networks. The experimental setup consists of a loop containing stainless steel elbow with simulated seawater of known concentration continuously flowing at a specific flow rate, thus allowing to study the effect of flow dynamics and salt concentration on corrosion. Electric field mapping setup is used to collect the voltage and current information along with the temperature of the elbow section. In addition to modeling, characteristics of the observed scale deposits are also studied in-depth and briefly reported in this work.