OBJECTIVE: Food allergy (FA) directly affects children's nutritional status, with a significantly higher risk of growth retardation among affected children. Identifying risk factors for FA and strategies to promote growth catch-up can offer valuable guidance for the treatment and nutritional management of children with FA. DESIGN: We developed machine learning models to predict the occurrence of immunoglobulin E-mediated food allergy (IgE-FA) and the likelihood of post-treatment growth catch-up, using demographic and biological baseline data. PATIENTS: We recruited 130 children aged 0-3 years with IgE-FA as the FA group and 65 healthy children as the control group. RESULTS: Using machine-learning-based bioinformatics analysis, we developed predictive models and identified key factors influencing growth in IgE-FA children. The IgE-FA prediction model achieved an area under the curve (AUC) of 0.78 (95% CI: 0.708-0.848). Greater birthweight, a family history of allergies, and early-life antibiotic exposure were identified as risk factors for IgE-FA. Notably, early antibiotic exposure increased the risk of IgE-FA by 2.77 times and the risk of milk allergy by 2.56 times. Growth analysis, both overall and by subgroup, revealed that pre-treatment weight strongly correlates with post-treatment height, weight, and body mass index (BMI), offering new perspectives for predicting and monitoring outcomes in IgE-FA. Milk allergy mainly impacts weight catch-up, whereas egg allergy affects BMI. Controlled avoidance of allergenic foods supports growth recovery in affected children. CONCLUSION: Growth in children with IgE-FA is often restricted, and achieving expected growth levels remains challenging even after treatment. Weight is a sensitive and accessible indicator for predicting IgE-FA and plays a key role in post-treatment growth catch-up. Early and personalized nutritional guidance, along with regular weight monitoring, is recommended for all children with food allergy.