The healthcare industry includes data production, storage, organization, processing, archiving, and destruction. It involves managing and supervising the lifespan of the healthcare sector. As technology advances, intelligent health monitoring systems gain popularity and importance. These days, physical infrastructure is being replaced by internet services in several industries, including healthcare. With the help of intelligent sensors and data collecting sources, the machine learning applications with Internet of Things (IoT) has transformed communication. Machine Learning (ML) capabilities in healthcare sector offers several chances to enhance patient outcomes. Personalized patient care is being revolutionized using machine learning (ML) in the healthcare sector. Massive patient data sets allow machine learning algorithms to find trends and forecast health outcomes with previously unattainable precision. Precision medicine, customized medications, real-time chronic condition monitoring, and predictive analytics for disease prevention are important application domains. ML algorithms are being employed in the healthcare industry more and more because of their ability to identify patterns in data. If diagnosticians employ ML to classify and treat patients, there could be a decrease in incorrect diagnosis. The objective of this chapter is to proposed a ML enabled IoT framework that can forecast the occurrence of disease based on certain parameters. This chapter focuses on the study of variety of ML techniques used in the healthcare sector. ML techniques like Regression model, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Naïve Bayes (NB) are extensively employed. The chapter deals with issues related to ML in healthcare applications. The goal is to remove the barriers standing in the way of fully utilizing ML in healthcare applications in view of three case studies i.e. heart failure prediction, Alzheimer’s Care and Chronic Kidney Disease (CKD). By understanding and addressing ML challenges, it would be possible to ensure that machine learning is successfully applied in the healthcare industry. Also, this chapter employs the implementation and utilization of ML algorithms in the healthcare sector and aim to address future research directions.