Proactive monitoring of one's health could avoid serious diseases as well as better maintain the individual's well-being. In today's Internet of Things (IoT) world, there has been numerous wearable technological devices to monitor/measure different health attributes. With the increasing number of attributes and wearables, it becomes unclear to individuals which ones they should be using. The aim of this paper is to provide a novel recommendation engine for personalized advised wearables and IoT solutions for any given individual. The way the engine works is through first identifying the diseases that this person is at risk of, given his/her attributes and medical history. This is done via analyzing the individual's unstructured medical history using text mining, adding it to his/her structured demographic attributes, and then feeding this data to a machine learning classification model that predicts eventual diseases. Then, we map these diseases to the attributes that need to be measured in order to monitor them. Lastly, we use a mathematical optimization model that we developed to recommend the optimal wearable devices and IoT solutions for the individual. Thus, our solution enables proactive health monitoring and can thus provide a significant human benefit.