Deadly infection outbreaks in confined spaces, whether it is a COVID-19 outbreak on a cruise ship or measles and stomach flu outbreaks in schools, can be characterized by their rapid spread due to the abundance of common spaces, shared airways, and high population density. Preventing future outbreaks and developing efficient mitigation protocols can benefit from advanced computational modeling approaches. Here, we developed an agent-based modeling approach to study the spatiotemporal dynamics of an infection outbreak in a confined environment caused by a specific pathogen, and to determine effective containment protocols. The approach integrates the 3D geographic information system of a confined environment, behavior of the hosts, key biological parameters about the pathogen obtained from the experimental data, and the general mechanics of host-pathogen and pathogen-fomite interactions. To assess our approach, we applied it to the historical data of infectious outbreaks caused by norovirus, H1N1 influenza A, and SARS-CoV-2 viruses. Our AI-GIS Infection Dynamics (AGID) model accurately predicted daily infection numbers, correctly identified the day when the CDC vessel sanitation protocol would be triggered, singled out key biological parameters affecting the infection spread, and propose pathogen-specific changes to existing containment protocols. Our work advances the understanding of infection spread on cruise ships while offering insights applicable to other similar confined settings, such as nursing homes, schools, and hospitals. By providing a robust framework for real-time outbreak modeling, this study proposes more effective containment protocols and enhances our preparedness for managing infectious diseases and emerging pathogens in confined environments.