Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The integration of Internet of Things (IoT) devices, Machine Learning (ML) algorithms, and cloud computing infrastructure is fundamentally transforming healthcare delivery through the creation of sophisticated analytics ecosystems that enable personalized patient care. This article shows the convergence of these technologies and their collective impact on modern healthcare systems, from real-time patient monitoring and predictive analytics to democratizing access for smaller healthcare providers. The article explores the technical architecture of IoT-enabled data collection systems, including wearable devices, clinical monitors, and environmental sensors, alongside the implementation of advanced ML algorithms for disease prediction, early detection, and treatment personalization. Cloud computing infrastructure serves as the backbone for these innovations, providing scalable platforms that process vast amounts of medical data while ensuring accessibility for healthcare organizations of all sizes. The article review also addresses critical challenges surrounding ethical considerations, regulatory compliance, and data security, particularly focusing on HIPAA requirements and the development of ethical frameworks for AI deployment in clinical settings. Through successful case studies and implementation examples, this work demonstrates how the synergistic application of these technologies achieves significant improvements in patient outcomes, operational efficiency, and healthcare accessibility while highlighting the necessity for robust governance frameworks to ensure responsible innovation in this rapidly evolving field.