Artificial intelligence is emerging as a central force in population level gastroenterology and hepatology research, largely due to its capacity to process large datasets with fast speed and pattern recognition. The field has seen progress in cancer surveillance, liver disease detection, and infectious disease monitoring, which are all areas of large global health concern. Screening for colorectal cancer benefits greatly from AI assisted polyp recognition, which reduces missed lesions and prompts earlier intervention. Machine learning is also improving assessment of metabolic dysfunction associated steatotic liver disease through reliable non invasive fibrosis detection. Population screening for hepatitis is becoming more efficient because AI models classify infection status with strong predictive power. Public health departments are adopting wastewater analytics guided by AI, which allows for earlier detection of community based gastrointestinal outbreaks compared to traditional reports. In upper gastrointestinal oncology, AI supported interpretation of endoscopic and histologic data increases diagnostic certainty. In inflammatory bowel disease, risk stratification models help identify patients likely to experience aggressive disease progression, which can shape preventive strategies before symptoms intensify. To address growth health disparities with the introduction of artificial intelligence into healthcare, there must be ethics involved in model design, rigorous model validation, and an equitable use of findings worldwide.