The healthcare industry is evolving at a quick pace, and that necessitates the integration of effective, scalable, and secure solutions that will manage the increasing volume of sensitive patient data. This is because of the aging population, increased cases of chronic diseases, and the necessity to track patients in real-time efficiently. To improve remote diagnosis and decrease reliance on hospital-based treatment, the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have been combined to allow constant monitoring of vital indicators such as heart rate, blood pressure, temperature, and oxygen saturation.However, there are still issues with current IoT-enabled healthcare systems, such as high communication latency, scalability restrictions, impacts about data privacy and security in cloud storage, and a lack of intelligence in analyzing complicated biomedical data. To overcome these issues, this paper proposes an improved Internet of Things (IoT) healthcare monitoring system that combines wearable biomedical sensors, a Raspberry Pi CPU, and fast 5G connectivity for data transfer in almost real-time. The system captures minor changes in biological signals by preprocessing physiological data and using Renyi Entropy to extract important characteristics. These are categorized using a hybrid Temporal Convolutional Network Bidirectional Long Short-Term Memory (TCN-BiLSTM) model that can recognize trends over the short and long term. For remote monitoring, data is safely kept in the cloud and accessed through a web app. Evaluation metrics such as encryption time, decryption time, response time and computation cost.