In a healthcare environment, Internet of Things (IoT) sensors’ devices are integrated to help patients and Physicians remotely. Physicians interconnect with their patients to monitor their current health situation. However, a considerable number of real-time patient data produced by IoT devices makes healthcare data intensive. It is challenging to mine valuable features from real-time data traffic for efficient recommendations to patients. Thus, an intelligent healthcare system must analyze the real-time health conditions and predict suitable drugs based on the diseases’ symptoms. In this paper, an IoT architectural model for smart health care is proposed. This model utilizes clustering and Machine Learning (ML) techniques to predict suitable drugs for patients. First, Spark is used to manage the collected data on distributed servers. Second, the K-means clustering algorithm is used for disease-based categorization to make groups of the related features. Third, predictor techniques, i.e., Naïve Bayes and random forest, are used to classify suitable drugs for the patients. Two standard Unique Client Identifier (UCI) machine learning datasets have been conducted in the experiments. The first dataset consists of different types of thyroid diseases, while the second dataset contains drugs with recommended medicines. The experimental results depict that the performance, i.e., the accuracy of the proposed model, is superior in predicting the suitable drugs for patients, by which it provides a highly effective delivery healthcare service in IoT. Random Forest correctly classified 99.23% instances while Naive Bayes results are 95.52%.

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