Machine learning has revolutionized medical technology through tiny machine learning (TinyML). With TinyML, machine learning models can be deployed on devices with minimal resources. Some examples are microcontrollers, wearables, and Internet of Things devices. TinyML creates unparalleled prospects for personalized, real-time healthcare applications by utilizing the power of edge computing and AI. This chapter aims to examine TinyML's potential to revolutionize medical monitoring and diagnosis by exploring its potential use in healthcare. We emphasized TinyML's tremendous influence on healthcare through many critical sectors, such as wearable devices, remote patient monitoring, drug development, diagnosis, and therapy. This chapter considers the challenges and opportunities associated with implementing TinyML in healthcare. The study continues with a look at the future of TinyML in healthcare. The work focuses on several TinyML use cases in the healthcare sector, emphasizing the necessity of researchers, practitioners, and policymakers working together to develop effective security solutions. In addition to highlighting the problems and issues in the field of TinyML in healthcare, the proposed study will also contribute significantly to future research and development.