Healthcare monitoring is the process of assessing an individual's physical, functional, or cognitive health using a range of approaches in order to detect changes, manage symptoms, and avoid serious health issues. Existing healthcare monitoring systems, on the other hand, face a number of challenges, including technical issues such as network connectivity and data security, patient-related barriers such as device adoption and digital literacy, system integration issues due to interoperability, and workforce constraints such as shortages and burnout. To address these issues, a Deep Learning-based TCN-BiGRU Healthcare Monitoring Framework for IoT is proposed (DL-TBH-IoT). Wireless sensors on the sensing layer collect physiological data, which is then aggregated by the connection layer and sent to the cloud layer. The cloud layer employs a Fuzzy Information System (FIS) to handle missing values and uncertainties before forecasting data with the TCN-BiGRU algorithm, which classifies patients as healthy or diseased based on medical information. The projected findings are delivered to patients, physicians, hospitals, or caretakers through the user application layer, allowing for fast intervention. The proposed technique is assessed against industry-standard performance indicators. The experimental findings show that DL-TBH-IoT achieves 98.5% accuracy, beating other approaches including EHMS (64%), FETCH (86%), and FIS (82%), guaranteeing effective and trustworthy healthcare monitoring in IoT contexts.