Humans live surrounded by a variety of threats. These threats can be diverse: natural, chemical, technological, psychological, social, ecological, global, biological, and health-related. The latter, namely biological and health threats, are the most pressing and dangerous from a human perspective as they affect living organisms’ health. Examples include infectious diseases caused by viruses, bacteria, or parasites (e.g., influenza, COVID-19, Lyme disease). In Poland, epidemic cases of the acute infectious respiratory disease COVID-19, caused by the SARS-CoV-2 virus, were first reported on March 4, 2020. The pandemic lasted three years. This scientific article attempts to present the architecture and functionality of the PandemIQ system. The system was designed for the early detection and forecasting of the regional risk of pandemic waves. The system is based on a hybrid artificial intelligence model that combines predictions based on epidemiological, mobility, and transportation data with interpretability (attention) mechanisms enabling visualization and decision justification. This paper provides a detailed discussion of the structure of decision reports for authorities, GIS-based operational dashboards (ArcGIS), and alternative solutions using the Apache ECharts library. It also presents validation procedures, data security aspects, and compliance with legal regulations (GDPR, AI Act). Simulation results using historical data on the spread of the SARS-CoV-2 virus indicate that the system can detect waves on average 7–14 days earlier than traditional epidemiological methods can, while maintaining a low false alarm rate.