The incorporation of Artificial Intelligence (AI) into epidemiological intelligence systems offers a revolutionary chance to improve pandemic response, biosecurity, and disease control. Due to its heavy reliance on human data collection, reporting, and analysis, traditional epidemiological surveillance frequently has delays, underreporting, and poor prediction ability. Real-time data processing from diverse sources, including electronic health records, social media, environmental sensors, and genomic surveillance, is made possible by AI-driven techniques including machine learning, natural language processing, and predictive analytics. By facilitating early epidemic detection, dynamic risk modeling, and focused intervention tactics, this integration enhances the promptness and precision of public health responses. This study explores integrating Artificial intelligence into epidemiological intelligence system to strengthen disease control, biosecurity and pandemic response adopting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The review systematically collated evidence on AI-enabled epidemiological intelligence systems, highlighting their contribution to early warning, outbreak management, and cross-sectoral coordination. Findings reveal that the use of AI improves biosecurity frameworks and enables proactive mitigation. Therefore, adopting AI-enhanced epidemiological knowledge strategically can improve pandemic preparedness, bolster national and international health security, and lessen the social and economic effects of infectious disease epidemics. This approach represents a critical evolution in public health practice, combining advanced computational tools with traditional epidemiological expertise to create resilient, adaptive, and proactive health intelligence systems.