Objective To clarify the specific clinical predictive efficacy of CT and serological indicators for the progression of connective tissue disease-associated interstitial lung disease (CTD-ILD) to progressive pulmonary fibrosis (PPF). Methods Patients who were diagnosed with CTD-ILD in Chest Hospital of Zhengzhou University Between January 2020 and December 2021 were recruited in the study. Clinical data and high-resolution CT results of the patients were collected. The patients were divided into a stable group and a progressive group (PPF group) based on whether PPF occurred during follow-up. COX proportional hazards regression was used to identify risk factors affecting the progression of CTD-ILD to PPF, and a risk prediction model was established based on the results of the COX regression model. The predictive efficacy of the model was evaluated through internal cross-validation. Results A total of 194 patients diagnosed with CTD-ILD were enrolled based on the inclusion and exclusion criteria. Among them, 34 patients progressed to PPF during treatment, and 160 patients did not progress. The variables obtained at lambda$1se in LASSO regression were ANCA associated vasculitis, lymphocytes, albumin, erythrocyte sedimentation rate, and serum ferritin. Multivariate COX regression analysis showed that the extent of fibrosis, serum ferritin, albumin, and age were independent risk factors for the progression of CTD-ILD to PPF (all P<0.05). A prediction model was established based on the results of the multivariate COX regression analysis. The area under the receiver operator characteristic curve at 6 months, 9 months, and 12 months was 0.989, 0.931, and 0.797, respectively, indicating that the model has good discrimination and sensitivity, and good predictive efficacy. The calibration curve showed a good overlap between predicted and actual values. Conclusions The extent of fibrosis, serum ferritin, albumin, and age are independent risk factors for the progression of CTD-ILD to PPF. The model established based on this and externally validated shows good predictive efficacy.