The present analysis investigates the predictive capacity of a fully automated coronary artery calcium (CAC) scoring system using artificial intelligence (AI) software (AVIEW, Coreline Soft, Seoul, Korea) in a low-dose computed tomography (LDCT)-based lung cancer screening (LCS) trial. The study includes 2239 participants from the Multicentric Italian Lung Detection (MILD) trial who underwent a baseline LDCT between September 2005 and January 2011, with a median follow-up of 190 months. The CAC scores were categorized into five strata, and their association with 12-year all-cause mortality and non-cancer mortality was analyzed. The results show that higher CAC scores, particularly CAC > 400, are associated with increased all-cause mortality and non-cancer mortality rates. The fully automated CAC scoring system effectively predicts all-cause mortality at 12 years in the LCS setting. These findings highlight the potential of automated CAC scoring in enhancing risk assessment and mortality prediction in LDCT-based LCS.