<![CDATA[The emergence of the COVID-19 pandemic catalyzed an unprecedented acceleration in the adoption of digital epidemiology and artificial intelligence (AI)-driven disease surveillance systems globally. Digital epidemiology harnesses non-traditional data streams—including social media analytics, mobile health applications, internet search trends, and satellite imagery—to augment conventional public health monitoring frameworks. This review critically evaluates the transformative impact of these technological innovations on public health practice in the post-pandemic era. We systematically examine the theoretical underpinnings, technological architectures, and real-world applications of AI-driven surveillance platforms, including machine learning-based outbreak prediction models, natural language processing systems for infodemic management, and geospatial modeling tools for epidemic mapping. Evidence synthesized from peer-reviewed literature published between 2018 and 2025 demonstrates that AI-integrated surveillance systems substantially reduce outbreak detection latency by 30–60% compared to traditional sentinel surveillance mechanisms. However, persistent challenges remain, including algorithmic bias, digital divide inequities, data privacy concerns, and interoperability limitations across heterogeneous health information systems. The review underscores the imperative for robust governance frameworks, equitable data infrastructure investment, and multi-sectoral partnerships to realize the full transformative potential of digital epidemiology. Recommendations for policymakers, public health practitioners, and technology developers are provided to guide the responsible integration of AI-driven surveillance into national and global health security architectures.]]>
