This review article explores how artificial intelligence is transforming public health approaches in gastroenterology and hepatology, which face rising global burdens from conditions such as colorectal cancer, metabolic liver disease, and viral hepatitis. The authors describe how AI-driven tools, including machine learning and deep learning models, are being used to improve disease detection, screening, and surveillance. For example, AI systems have enhanced colorectal cancer screening by identifying high-risk individuals who might be overlooked by traditional methods, and non-invasive AI models are improving early detection of liver fibrosis in metabolic-associated steatotic liver disease. The review also highlights how AI is advancing wastewater surveillance to monitor gastrointestinal infections and viral hepatitis outbreaks before they appear in clinical data. Despite these benefits, the authors emphasize ongoing challenges such as limited data diversity, ethical and privacy concerns, unequal access to AI technologies, and the lack of clear regulations guiding implementation in real-world public health settings.