Public health systems, socioeconomic stability, and national security are all under constant and growing threat from emerging infectious diseases. Particularly among highly mobile and environmentally sensitive populations, traditional epidemiological monitoring techniques, which mostly rely on manual reporting and delayed laboratory confirmation, frequently fail to identify epidemics in their early phases. The potential of artificial intelligence (AI)-driven epidemiological monitoring as a revolutionary strategy for the early identification of newly emerging infectious diseases and the reinforcement of national health security is examined in this paper. This summarizes recent developments in AI applications for epidemiological intelligence, emphasizing how automated risk scoring, pattern recognition, and spatiotemporal modeling might spot aberrant illness trends before they spread widely. The study looks at how AI-driven surveillance helps national health security by facilitating better coordination between public health, border control, and emergency management organizations, as well as proactive readiness and quick response decision-making through Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Despite these benefits, there are issues with data quality, interoperability, algorithmic bias, ethical governance, and data privacy when implementing AI-based surveillance   systems. These restrictions are particularly noticeable in low- and middle-income nations, where technical capacity and digital infrastructure may be limited. All things considered, AI-driven epidemiological monitoring is a significant development in contemporary disease intelligence, providing strategic value for early outbreak identification, pandemic prevention, and national health security resilience in a world growing more interconnected by the day.