aspg-18-3037-1792025846 20261015005726 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 13 2 Kiran Sree Pokkuluri Professor & Head, Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India Vibha Tiwari Asst. Professor, Centre for Artificial intelligence, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India Jyoti Uikey IES Institute of Pharmacy, IES University, Bhopal, Madhya Pradesh 462044, India Prerna Mehta Department of Biotechnology, GD Rungta College Of Science & Technology, Kohka Kurud Road, Bhilai, Chhattisgarh 490024, India Chopparapu Srinivasa Rao Asst. Professor, Dept. of CSE, Lakireddy Bali Reddy College of Engineering, Vijayawada, AP, India Annamaraju Thanuja Assistant Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank. 2024 212 222 3037 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.130217 https://americaspg.com/journal/18/article/3037 https://americaspg.com/storage/51720978169.pdf