Objective: To analyse the factors influencing the occurrence of poor prognosis in patients with sudden deafness with hypertension, to construct a prediction model based on four machine learning algorithms, and to compare the predictive performance of different models. Methods: The study design was retrospective. Sudden deafness patients with combined hypertension who attended the Department of Otorhinolaryngology, Second Affiliated Hospital of Zhengzhou University during February 2023 to May 2024 were selected as study subjects. Patients" clinical data were collected through the hospital"s electronic medical record system. Patients were divided into effective and ineffective groups according to hearing recovery after treatment, and predictor variables were examined using one-way analysis, least absolute shrinkage and selection operator (LASSO regression), and Boruta algorithm. Patients were randomly assigned to training and validation sets in an 8:2 ratio. Based on the training set data, four machine learning algorithms [logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost) and support vector machine (SVM)] were used to construct the prediction models; based on the validation set data, the area under the curve (AUC) of the subjects" working characteristic, sensitivity, specificity, accuracy and F1 score of the four models were compared. The Delong test was used to compare the AUC of each model in the test set and to determine the best model.? Results: A total of 232 patients were included in the study. Univariate analysis, LASSO regression and the Boruta algorithm identified a total of 7 variables (the degree of hearing loss, audiogram type, duration of diabetes mellitus, and the course of hypertension,etc.) that were strongly associated with poor prognosis must be taken into account. The predictive performance of the four models was validated based on the validation set data, and it was found that the Xgboost model (AUC=0.787, 95% CI: 0.642-0.931) exhibited the most favourable predictive performance, and the difference in AUC between the ROC curves of the four models was not statistically significant.? Conclusion: The risk of poor prognosis for sudden deafness in combination with hypertension is influenced by a number of factors, including initial hearing level, audiogram type, diabetes mellitus, duration of hypertension, smoking, and hyperuricemia, among others. The four machine learning models demonstrated satisfactory predictive performance, with the XGboost model exhibiting the most optimal predictive performance.