Stiffened panels are renowned for their exceptional load-bearing capabilities due to their high strength-to-weight ratio and are extensively utilized in ship and marine structures. Nevertheless, they are susceptible to failure under compression, particularly in marine environments where corrosion leads to material and structural degradation. Stiffener corrosion was not involved adequately in current studies, with an unclear synergistic effect of corrosion damage with structural configurations. This paper introduces a numerical modeling approach to develop finite element (FE) models of panels with random pitting corrosion, considering both corrosion on the plating and stiffeners. These FE models are validated against existing experiments and empirical formulae and employed to investigate the effect of random pitting damage on the ultimate strength of panels. The study examines the impact of various structural parameters, such as plate aspect ratio, plate slenderness ratio, stiffener slenderness ratio, and the height-to-thickness ratio of stiffener web, on ultimate strength under different levels of pitting damage. Additionally, the load-bearing behavior and failure modes of damaged panels are analyzed. An artificial neural network (ANN) is developed to predict the ultimate strength of pitted panels. The findings indicate that the ultimate strength of panels with stiffener corrosion in the form of random pitting corrosion leads to a more significant reduction by about 17.7% than that without stiffener corrosion, potentially altering the failure mode. The proposed ANN model accurately predicts the ultimate strength of pitted panels with and without stiffener corrosion, with a mean relative error of approximately 1.1% compared to FE analysis results.