The integration of Internet of Things (IoT) technology into healthcare systems has introduced new possibilities for early and accurate disease detection [1]. This research investigates the role of IoT in identifying a wide spectrum of diseases, including cardiovascular disorders, diabetes, respiratory conditions, infectious diseases, and neurological impairments [2]. A key focus is on optimizing sensor selection to improve diagnostic accuracy, reduce energy consumption, and ensure reliable real-time monitoring [3]. We propose a comprehensive IoT-based framework that maps disease profiles to appropriate sensors, integrates AI-driven analytics, and supports adaptive health surveillance. The proposed system aims to enhance scalability, interoperability, and precision in patient care. This work contributes to the development of intelligent, efficient, and patient-centric IoT solutions in healthcare monitoring.