Artificial Intelligence (AI) is transforming public health and epidemiology by enabling earlier detection, improved surveillance, predictive forecasting, and more efficient responses to health threats. Leveraging techniques such as machine learning, deep learning, natural language processing, and computer vision, AI can process vast and diverse data sources, including electronic health records, mobile health apps, genomic sequencing, and social media. These tools enhance outbreak prediction accuracy, optimize vaccine distribution, accelerate contact tracing, and map disease transmission, as demonstrated during the COVID-19 pandemic. Beyond infectious disease, AI also supports monitoring of non-communicable diseases and mental health through passive data collection and behavioral trend analysis. Despite its promise, barriers hinder widespread, equitable adoption. Key concerns include data privacy, algorithmic bias, lack of transparency, and the digital divide, which risk worsening health disparities if not addressed. Effective integration of AI into public health requires robust governance frameworks, cross-sector collaboration, and workforce capacity-building. Looking forward, federated learning, explainable AI, and strong regulatory mechanisms will be essential to ensure ethical, accountable, and globally inclusive use. By critically assessing current applications and charting future priorities, this study underscores how AI can strengthen health systems to be more responsive, evidence-driven, and equitable worldwide.