This research applies radiomics analysis to predict overall survival in patients with chronic obstructive pulmonary disease (COPD). The study, which includes 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort of 112 patients, identifies five features through the least absolute shrinkage and selection operation (LASSO) Cox regression analysis. These features form a radiomics signature (RS), which effectively classifies COPD patients into high or low mortality risk groups. The RS demonstrated a good performance, with a C-index of 0.774 in the discovery group and 0.805 in the validation group. The high-risk group identified by the RS exhibited worse overall survival. Overall, the study confirms the feasibility of using a radiomics approach for survival prediction and risk stratification in COPD patients, with the radiomics model showing promising performance. This highlights the feasibility of a radiomics approach for survival prediction and risk stratification in COPD patients, and the potential utility of the AVIEW COPD in this regard.