Machine learning (ML) is a strong means to bring perspectives concealed on the internet of things (IoT) data. Such composite fields act cleverly to improve the decision-making procedure in several domains, such as teaching, security, industry, and the biomedical sector. ML enables the IoT to explain obscured structures in bulk data for best forecasting and recommendation systems. Medicine has adopted IoT, ML, and deep learning (DL); hence, automated machines could build medical records, diagnose diseases, and, overmuch significantly, carry on timely observing patients. ML and DL algorithms act diversely on various datasets. Since the predictive outcomes differ, this could affect the general outcomes. The difference in forecasting outcomes seems significant in the clinical decision-making procedure. Thus, it is all-important to realize the variant ML and DL algorithms applied to deal with IoT data in medicine. This chapter spotlights known ML and DL algorithms for classification and forecasting and presents their applications in medicine. The aim of this chapter is to show a broad summary of current ML and DL techniques and their application in IoT biomedical data. In a careful investigation, we find that diverse ML and DL forecasting algorithms have different defects. Counting on the form of IoT datasets, we postulate to select the best way to forecast vital biomedical data. This chapter gives several cases of IoT-based ML and DL. While DL procedures have assisted these tasks, the integration with IoT still requires further development.