Integrating Artificial Intelligence of Every Medical Thing (AIoEMT) into personalized medicine represents a transformative shift toward individualized, data-driven healthcare. This research explores the AIoEMT framework’s applications in optimizing diagnosis, treatment, and real-time therapeutic adjustments. By leveraging advanced AI techniques such as machine learning, deep learning, and natural language processing, AIoEMT processes vast, heterogeneous datasets to uncover patterns that guide clinical decision-making. Key applications discussed include personalized cancer therapies, adaptive cardiovascular care, dynamic diabetes management, and AI-guided pharmacogenomics. The study also examines the role of data analytics in enhancing decision support systems, enabling proactive, evidence-based care. Furthermore, the research highlights data privacy challenges, algorithmic bias, and system interoperability, offering potential solutions such as federated learning and explainable AI. Real-time monitoring technologies, predictive models, and digital twin simulations demonstrate the potential of AIoEMT to predict disease trajectories and optimize treatments in dynamic environments. The findings underscore the transformative impact of AIoEMT on personalized medicine, enhancing treatment precision, patient outcomes, and healthcare system efficiency. As AI technologies continue to advance, AIoEMT is a critical enabler of precision medicine in the era of smart healthcare systems.