This study examines the transformative potential of healthcare analytics when combined with Machine Learning (ML) and the Internet of Things (IoT). It highlights how these technologies can revolutionize patient care, diagnosis, and operational efficiency. ML excels at analyzing vast datasets to improve diagnostic precision and personalize treatments by identifying patterns and trends, while IoT devices enable real-time patient monitoring and operational optimization, supporting timely interventions and resource management. However, integrating ML and IoT into healthcare comes with challenges, including concerns around data privacy, model transparency, interoperability, and biases in technology. To address these issues, the study advocates for standardized data-sharing protocols, advanced encryption techniques, and the adoption of interpretable ML models to ensure ethical and practical integration into healthcare systems. The study also emphasizes the importance of patient-centered approaches, interdisciplinary training programs, and globally coordinated efforts to address health inequities and leverage the benefits of these technologies. By combining innovative solutions with ethical considerations, this paper provides actionable insights into the intelligent and equitable application of ML and IoT in healthcare. It concludes by offering a forward-looking perspective on the integration of these technologies to deliver more efficient, personalized, and ethical healthcare solutions.