Objective To analyze the influencing factors for the poor prognosis in patients with sudden sensorineural hearing loss (SSNHL) accompanied with hypertension, construct prediction models based on four machine learning algorithms, and compare the prediction effects of different models.Methods Patients with SSNHL combined with hypertension admitted to the Department of Otorhinolaryngology of the Second Affiliated Hospital of Zhengzhou University from February 1, 2023 to May 31, 2024 were selected as the research subjects. The clinical data of patients were collected through the hospital's electronic medical record system. According to the hearing recovery after treatment, the patients were divided into the effective group and the ineffective group. Univariate analysis, the least absolute shrinkage and selection operator (LASSO regression), and the Boruta algorithm were used to screen the predictive variables. Four machine learning algorithms, namely logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were adopted to construct prediction models and conduct verification. The predictive performances of the four models were compared. The Delong test was employed to compare the area under the curve (AUC) of the models in the validation set, and the evaluation indicators of each model in the validation set were compared to determine the optimal model. Moreover, the Shapley additive explanation (SHAP) algorithm was utilized to conduct explanatory analyses on the models.Results A total of 232 patients were included in this study, and 7 variables closely related to poor prognosis were screened out, including the degree of hearing loss, audiogram type, diabetes mellitus, and the course of hypertension. The predictive performance of the four models was verified. Among them, the XGBoost model demonstrated the best overall predictive performance, with an AUC of 0.787, an accuracy of 78.56%, a precision of 78.95%, a recall of 71.43%, and an F1 score of 75%. Moreover, there were no statistically significant differences in the AUC among the receiver operator characteristic curves of the four models (P>0.05).Conclusions The risk of poor prognosis in SSNHL patients with hypertension is affected by multiple factors, including initial hearing level, type of audiogram, diabetes mellitus, duration of hypertension, smoking and hyperuricemia. All the four machine learning models have good predictive performance, with the XGboost model being the optimal.