Objective To explore the influencing factors of children with severe mycoplasma pneumoniae pneumonia (SMPP) complicated with hypoxemia, and to construct and verify the nomogram risk prediction model. Methods A retrospective analysis was conducted on the clinical data of 218 infants with SMPP in Nanyang Central Hospital from December 2022 to December 2025. Depending on whether they were complicated by hypoxemia, they were split into two groups: the hypoxemia group (102 instances) and the normal group (116 cases). The clinical data of the two groups were compared, and the independent risk factors were screened by binary Logistic regression analysis. Based on the screening results, a Nomogram prediction model was constructed, and the model efficiency was evaluated by receiver operator characteristic (ROC) curve and Bootstrap calibration curve. Results Among 218 cases of SMPP, 102 cases were complicated with hypoxemia, the incidence rate was 46.79%. Multivariate analysis showed that extrapulmonary complications, bronchiolitis imaging classification, elevated ferritin (FER), elevated D-dimer (D-D) and elevated serum creatinine (Scr) were independent risk factors for SMPP children with hypoxemia (all P＜0.05). The Nomogram model based on the above five indexes showed that the area under the curve (AUC) was 0.874 (95%CI: 0.822-0.915), the sensitivity was 80.39%, and the specificity was 81.90%; Hosmer-Lemeshow test results showed that χ2＝4.415, P＝0.818, suggesting that the model had a good goodness of fit; Bootstrap’s 1, 000 sampling tests showed that the predicted value of the model was in good agreement with the actual situation. Conclusion Extrapulmonary complications, imaging classification of bronchiolitis, FER, D-D and Scr are independent risk factors for hypoxemia in children with SMPP. The nomogram prediction model based on the above indicators has good prediction efficiency, which can provide reference for early clinical identification of high-risk children with SMPP complicated with hypoxemia and formulation and optimization of individualized prevention and treatment strategies.