The rapid detection of disease outbreaks is critical in mitigating their impact on public health, a need that has become even more pronounced in light of recent global events. This research presents a novel, IoT-driven intelligent model that integrates both environmental and physiological data to assess and predict outbreak risks in real-time. Our model incorporates key environmental factors—such as air quality, ambient temperature, and humidity alongside essential physiological parameters including body temperature, respiratory rate, heart rate, and systolic blood pressure. By applying different weights to these parameters, the model employs intelligent analytics to compute a comprehensive risk score, categorizing zones as high, medium, or low risk. As new physiological data is gathered through IoT-enabled smart devices and sensors, the system continuously analyzes the information for anomalies that might indicate a heightened outbreak risk. When significant deviations are detected, the model triggers alerts, recommending precautionary measures such as increased monitoring or quarantine protocols. To validate the accuracy and effectiveness of this approach, we compared our model’s predictions with actual COVID-19 case data from India during the same timeframe. The results demonstrated a strong alignment, highlighting the model’s potential to provide early warnings for disease outbreaks. This intelligence-driven, real-time surveillance system marks a significant improvement over traditional outbreak detection methods, which often rely on delayed reporting and also only on physiological parameters. By integrating environmental conditions with real-time physiological data, this approach offers a more holistic and proactive method of disease surveillance, ultimately enabling faster and more targeted public health responses.