Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia (BPD) in preterm infants with gestational age < 32 weeks. Methods Clinical data of 472 preterm infants with gestational age <32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed. Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1, 7, 14, 21, and 28. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow test. Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group (n=147) and the non-BPD group (n=325) (P<0.05). Predictors of BPD evolved across time points: on day 1, key predictors included gestational age, birth weight, Score for Neonatal Acute Physiology II (SNAP-II), invasive mechanical ventilation, and fraction of inspired oxygen >30%; by day 7, additional variables emerged, including fasting duration >2 days, mean feeding advancement rate <8.5 mL/(kg·d), neonatal respiratory distress syndrome, apnea of prematurity, and positive sputum culture; from day 14 onward, nutrition- and treatment-related indicators were incorporated additionally. The models demonstrated good discrimination at postnatal days 1, 7, 14, 21, and 28, with AUCs of 0.917, 0.927, 0.939, 0.944, and 0.968, respectively, and good calibration (Hosmer-Lemeshow P>0.05). Internal validation showed AUCs ranging from 0.899 to 0.958, indicating robust performance. Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors, respiratory support, nutritional management, and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age < 32 weeks.