The rapid evolution of Internet of Medical Things (IoMT) has enabled continuous monitoring and automated control of drug delivery systems, transforming modern healthcare services. Cloud-based intelligent drug delivery monitoring systems integrate IoT medical devices such as smart infusion pumps, insulin delivery units, and wearable sensors with scalable big data analytics platforms to manage large volumes of heterogeneous patient data. However, most existing systems rely primarily on real-time monitoring and threshold-based alerts, limiting their ability to anticipate patient-specific dosage requirements. This paper proposes a cloud-based intelligent drug delivery monitoring system using big data analytics that employs predictive analytics to forecast patient conditions and dynamically adjust medication dosages. The proposed framework leverages historical clinical records, real-time physiological data, and contextual information to generate personalized dosage recommendations. Experimental evaluation demonstrates that the predictive analytics–driven approach improves medication adherence, reduces unnecessary hospital readmissions, and enhances the precision of clinical outcomes compared to conventional monitoring systems. The results highlight the potential of cloud-enabled big data intelligence in delivering proactive, patient-centric, and scalable drug delivery solutions.
Keywords: Cloud Computing; Intelligent Drug Delivery Systems; Big Data Analytics; Predictive Analytics; Internet of Medical Things (IoMT); Smart Healthcare; Patient Monitoring Systems.
How to cite this article:Nandigama NC.; Cloud-Based Intelligent Drug Delivery Monitoring System Using Big Data Analytics .Int J Drug Deliv Technol. 2026;16(1s): 739-743; DOI: 10.25258/ijddt.16. 739-743