Objective To develop and validate a nomogram model that can be used to predict the prognosis of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients with type II respiratory failure. Methods A retrospective analysis was conducted on the clinical data of 300 hospitalized AECOPD patients in the People’s Hosipital of Leshan from August 2016 to December 2021. Patients were grouped into a training cohort (n=210) and a validation cohort (n=90) in a 7:3 ratio. The variables for the patients in the training cohort were selected using the least absolute shrinkage and selection operator (LASSO), followed by multivariate logistic regression analysis to identify independent risk factors of poor prognosis in AECOPD with type II respiratory failure, and a nomogram model was constructed. Receiver operating characteristic (ROC) curves were plotted for the training and validation cohorts, and the area under ROC curve (AUC) was calculated.The model was validated by conducting the Hosmer-Lemeshow test, drawing calibration curves, and performing decision curve analysis(DCA).ResultsCardiovascular disease, lymphocyte percentage, and red cell distribution width-standard deviation(RDW-SD) were identified as independent risk factors of poor prognosis for AECOPD patients with type II respiratory failure (P<0.05). The AUC values for the training and validation cohorts were 0.742 (95%CI: 0.672-0.812) and 0.793 (95%CI: 0.699-0.888), respectively. The calibration curves of the two cohorts are close to the desirable curves.The Hosmer-Lemeshow test P-values were greater than 0.05, indicating good clinical practicality. The DCA curve indicates that the model has good clinical value. Conclusions The clinical prediction model, based on factors such as cardiovascular disease, lymphocyte percentage, and RDW-SD, showed good predictive value for AECOPD patients complicated by type II respiratory failure.