This research paper attempts to identify the Blood pressure based BP Systolic and BP Diastolic data and to cross validate the changes of Blood pressure using various machine learning method. The data were collected from secondary source containing 460 patients. The case sheet deals with demographic characteristics, Blood Pressure, Fat, Liver and diabetic parameters. This study concentrates on age, BP Systolic and diastolic only. Machine learning methods such as Logistic Regression, Support Vector Machine and Random Forest Model were used as data mining tools to explore the classification model and to cross validate the present dataset. All the three classification models were applied and extracted. Area under the Curve, Classification Accuracy, F1 Score, Precision and Recall are all closer to unity. The above measures shows three major categories of classification based on Blood pressure parameters. Machine learning methods achieved best model and are labeled as Normal, Elevated and Hypertension. The results of the present study indicate that the machine Learning Data Mining Tools can be used as a feasible tool for the analysis of large set of Blood pressure data. Finally, the three model classification is visualized using Silhouette plot.