The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things (IoT) 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 the energy consumption. These packets are captured by our prototype ESP32-based gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides doctors 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. Advertising once per second reduces energy consumption by 50%, with further halving the sampling rate boosting savings up to 90%. Our architecture maintains robust data confidentiality and efficient storage, enabling effective cloud-driven visualization. This study validates the feasibility of encrypted advertising packets for secure, stable, scalable and efficient biometric IoT data acquisition, offering potential for advancements in remote healthcare monitoring and broader biomedical research environments.