This study focus on utilizing the strength of Decision Tree Algorithm to develop a High Blood Pressure prediction model with the help of RapidMiner studio. A dataset containing 2000 records of patients with high blood pressure was collected from National Health and Nutrition Examination Survey (NHANES). The dataset was properly prepared and feature selection algorithms where used to determine the most relevant feature from the dataset. The features used in the course of the study are chronic kidney disease, adrenal and thyroid disorder, level of hemoglobin, genetic pedigree coefficient, age, alcohol conception, sex, BMI, and salt conception.  The data set was split into two parts, training data set and testing data set. The training data set consist of 80% of the data while the testing data set contains 20% of the data. The decision tree model was trained with the training dataset and the developed model was applied to the test dataset. The performance of the model was further evaluated and produced an accuracy of 87% which shows that the Decision Tree algorithm can be effective on the prediction of High Blood Pressure.