This paper discusses a model developed and applied to evaluate the probability of Stress Corrosion Cracking (SCC) failure in a large gas pipeline system spanning approximately 8,500 miles. A machine learning algorithm (neural network) was applied to the system, which has experienced nearly 500 prior instances of SCC. Subject matter experts were interviewed to help identify key system factors that contributed to the prevalence of SCC and these factors were incorporated in the neural network algorithm. Key factors such as coating type, vintage, operating stress as a percentage of SMYS, distance to compressor station, and seam type were evaluated in the model for correlation with SCC occurrence. A Bayesian analysis was applied to ensure the model aligned with the prevalence of SCC encountered. A Probabilistic Fracture Mechanics (PFM) model was then applied to relate the probability of SCC existing to the probability of rupture.