Aluminum alloy panels joined with stainless steel fasteners have been known to occur in aerospace structures, due to their respective optimized mechanical properties. When connected via a conductive solution, a high-driving force for galvanic corrosion is present. The combination of the dissimilar materials, indicating galvanic corrosion, and complex geometry of the occluded fastener hole, indicating crevice corrosion, leads to the detrimental combined effect of galvanic-induced crevice corrosion, as investigated previously in Part I. The present work extends the validated finite element method (FEM) model to predict the current distribution and magnitude in a variety of geometric and environmental conditions, with the goal of preventing corrosion damage within the highly-susceptible fastener hole. Specifically, water layer thicknesses ranging from bulk full-immersion (800 μm) to atmospheric (89 μm) conditions was investigated, as well as the impact of external scribe dimensions. Two avenues for mitigation were determined, 1) to force the majority of current away from the fastener hole and onto the bulk surface of the panel, and 2) to lower the overall galvanic coupling current. A random forest machine learning algorithm was developed to generalize the FEM predictions and create an open-source applicable prediction tool.