The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these gaps by investigating and evaluating an IoT framework leveraging lightweight communication and real-time visualization for improved healthcare monitoring. Drawing primarily on recent peer-reviewed journals and reputable conference proceedings, we evaluate an IoT architecture that securely integrates wearable biometric data into a cloud-based dashboard. The system utilizes encrypted advertising packets (e.g., AES-128-CCM) to broadcast biometric signals, eliminating the need for permanent device pairing and minimizing energy consumption. These packets are captured by our prototype ESP32-based (Espressif Systems, Shanghai, China) gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides medical staff and end-users with real-time insights and long-term data tracking. Emphasis was placed on evaluating the system’s low latency performance, energy efficiency and data confidentiality. System evaluation demonstrates that encrypted advertising packets can securely transmit biometric signals, while drastically reducing energy consumption and latency. System evaluation demonstrates that encrypted BLE advertising serves as a superior alternative to traditional pairing-based methods for long-term medical monitoring. By implementing a dual-optimization strategy that balances data confidentiality with power efficiency, the proposed system achieved a 33-fold increase in operational autonomy compared with standard permanent BLE connections. These results represent a significant advancement in battery longevity for the IoMT ecosystem, providing a scalable solution for continuous, secure biometric signal transmission with minimal energy overhead.