Public health organizations play a crucial role in preventing diseases, promoting health, and addressing disparities within communities. However, their ability to monitor health trends and respond effectively to emerging threats is often obstructed by fragmented and incomplete data. This paper proposes a holistic, data-driven approach to enhance the capabilities of public health agencies in disease surveillance and community health assessment. By leveraging advanced data acquisition, AI-driven analysis, and intuitive data visualization tools, the proposed system enables public health professionals to gather, analyze, and interpret large volumes of health, environmental, and personal data in real time. The system was deployed across five cities globally, focusing on Type 2 Diabetes (T2D) as a case study. Data was collected from 1,832 participants using mobile applications, wearable devices, and environmental sensors. This approach demonstrated the potential to generate actionable insights, improve public health decision-making, and foster personalized health interventions.