Hypertension is a common chronic disease. Early detection and early measures can reduce the risk of complications. Although the occurrence and development of hypertension are related to many factors, diet is recognized as one of the main factors affecting hypertension. Machine learning models can effectively predict the disease and provide adjuvant therapy. Accordingly, this paper proposes a scheme based on XGBoost to predict hypertension by analyzing nutritional ingredients. The scheme consists of five parts: data conversion, feature selection, data cleaning and standardization, model building, classification and evaluation. The experimental results show that XGBoost obtains an F1_score of 0.859 in the prediction of and the accuracy rate exceeds 85%, which are higher than random forests, support vector machine and artificial neural network. By analyzing the influence factors of different nutritional ingredients on the prediction of hypertension, we obtained the top 10 nutritional characteristics that affect hypertension, most of which are the same as medical conclusions, verifying the effectiveness of the model.