The Internet of Things (IoT) is revolutionizing healthcare by providing real-time data on vital health indicators, reducing hospital visits, and promoting patient engagement through wearable technology. IoT technologies enhance treatment programs, medication adherence, and healthy lifestyles. Data analysis is crucial for e-health programs, enhancing patient outcomes, simplifying healthcare management, and advancing medical innovation. It aids in creating prediction models for disease susceptibility. This can help therapies halt or limit the spread of disease, ultimately leading to improved population health outcomes. Healthcare professionals rely on high-quality, consistent data for informed patient care, but heterogeneous data issues can impact the accuracy and reliability of this information. The operational facets of healthcare services are impacted by data quality. Efficient scheduling, inventory control, and resource allocation are made possible by reliable data. Precise information regarding patient admissions and discharges, for instance, aids in maximizing bed utilization and staffing ratios, cutting down on wait times, and enhancing overall service provision. Principles of trust and data quality are especially crucial when the IoT cloud system is pre-processing. In order to process data and respond to emergencies, this study uses architecture based on hybrid IoT cloud computing, in which smart devices and patient records are gathered and stored in real-time. Maintaining a balance between various data quality aspects is crucial to ensure data is suitable for its intended use without incurring unnecessary expenses or operational constraints, despite the frequent trade-offs between these aspects. Data analysts play a crucial role in healthcare operations, upholding ethical standards in research. The quality of data is essential for clinical judgment, research ethics, and overall system functioning. The study aims to apply trust and data quality models to prepare data for analysts, ensuring accurate clinical decisions. An all-encompassing strategy for enhancing data dependability and usability is developed in this study by investigating several trust models and data quality frameworks. In the end, the study hopes to improve patient outcomes and the efficiency of healthcare services by tackling typical problems with data quality that will increase the efficacy of data-driven efforts in the healthcare industry.