Past research in tribocorrosion mainly relies on costly and trial-and-error experimental methods to study the materials’ deformation and degradation under extreme conditions. This work developed a finite element based multiphysics model, validated by existing tribocorrosion experiments of two Al alloys, to analyze the synergistic effects of mechanical and corrosion properties on the material degradation mechanisms of tribocorrosion. During consecutive passes of the counter body, significant residual stress was found to develop near the edge of the wear track, leading to highly concentrated corrosion current than elsewhere. Such non-uniform surface corrosion and stress-corrosion coupling led to variations of tribocorrosion rate over time, even though testing conditions were kept constant. Tribocorrosion rate maps were generated to predict material loss as a function of different mechanical and electrochemical properties, indicating a hard, complaint metal with high anodic Tafel slope and low exchange current density is most resistant to tribocorrosion. Finally, two surrogate models, Gaussian process and neural network with dropout, were used for uncertainty quantification of the finite element model.