The study constructed and validated a prediction model based on treatment efficacy in patients with laryngopharyngeal reflux (LPR) and allergic rhinitis (AR) to provide a tool for individualized treatment decision making. It included 106 patients with AR admitted to the hospital from January 2023 to November 2024 who were randomly divided into a training set (n=73) and a validation set (n=33) in a ratio of 7:3. Independent factors were identified by univariate and multivariate logistic regression to create a nomogram. Multivariate analysis showed that body mass index (BMI), duration of AR, number of allergens and total serum IgE were independent predictors of treatment efficacy (p < 0.05). The baseline data of the training and validation sets were balanced (p > 0.05). The area under the ROC curve (AUC) reached 0.809 (95% CI: 0.693–0.924) in the training set and 0.823 (95% CI: 0.630–1.000) in the validation set, with a good fit of the calibration curve. The nomogram effectively predicts treatment efficacy in patients with LPR complicated by AR.