Background: Plastic bronchitis (PB) is a severe condition requiring early identification and treatment. The aim of this study is to analyze the clinical characteristics and risk factors for PB in pediatric Mycoplasma pneumoniae pneumonia (MPP), and to develop and validate a nomogram model for prediction. Methods: A retrospective analysis involved clinical data from 421 children diagnosed with MPP who underwent fiberoptic bronchoscopy in Wuxi Children’s Hospital from January 2022 to December 2023. Basing on bronchoscopic findings, 90 children were assigned into PB and 331 to non-PB. For external validation, the study considered 354 children diagnosed with MPP between January and May 2024 at the same hospital. The study identified independent risk factors for PB using LASSO and multivariate logistic regression and constructed and validated a visual predictive model. Bootstrap was used for internal validation. ROC curves, calibration curves and DCA curves assessed the model. An online dynamic nomogram tool was also launched for clinician use. Results: Children in the PB group experienced longer hospital stays, higher fever peaks, and required more oxygen therapy compared to the non-PB group. They also showed symptoms of shortness of breath, depression, reduced lung sounds, and elevated levels of ANC, NLR, CRP, ALT, AST, LDH, CK, INR, D-D, as well as increased incidences of pneumonia consolidation, pleural effusion, and atelectasis (all P<0.05). Factors such as oxygen therapy, fever duration, atelectasis, NLR, CK, and D-D significantly influenced PB development (all p<0.05). The ROC curve showed that the nomogram model had an AUC of 0.88 (95% CI: 0.84-0.93), with a sensitivity of 94.74% and a specificity of 74.63%. In the validation cohort, the AUC was 0.9 (95% CI: 0.84-0.98), with a sensitivity of 84.44% and specificity of 80.97%. Calibration curves showed strong consistency between predicted and actual PB occurrence rates. DCA confirmed the significant net benefit of the predictive model. Conclusion: This study developed and validated a predictive model for PB in pediatric MPP, using key factors such as oxygen therapy, fever duration, atelectasis, NLR, CK, and D-D. The model demonstrates excellent predictive accuracy and offers a useful tool for clinicians, enhancing early PB identification.