This paper gives an insight on how to use big data analytics for developing effective health recommendation engine by analyzing multi structured healthcare data. Evidence-based medicine is a powerful tool to help minimize treatment variation and unexpected costs. Large amount of healthcare data such as Physician notes, medical history, medical prescription, lab and scan reports generated is useless until there is a proper method to process this data interactively in real-time. In this world filled with the latest technology, healthcare professionals feel more comfortable to utilize the social network to treat their patients effectively. To achieve this we need an effective framework which is capable of handling large amount of structured, unstructured patient data and live streaming data about the patients from their social network activities. Apache Spark plays an effective role in making meaningful analysis on the large amount of healthcare data generated with the help of machine learning components and in-memory computations supported by spark. Healthcare recommendation engine can be developed to predict about the health condition by analyzing patient’s life style, physical health factors, mental health factors and their social network activities. Machine learning algorithms plays an essential role in providing patient centric treatments. Bayesian methods is becoming popular in medical research due its effectiveness in making better predictions.For example on training the model with the age of women and diabetes condition helps to predict the chances of getting diabetes for new women patients without detailed diagnosis.