The paper “Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities” examines how artificial intelligence (AI) can improve public health systems across Africa, particularly in low-resource settings. It explores how machine learning and other AI techniques are being used for disease detection, outbreak prediction, real-time surveillance, and health resource management. The authors focus on major public health challenges such as HIV, cholera, Ebola, measles, tuberculosis, malaria, COVID-19, and mental health. Through numerous case studies, the paper shows that AI can enhance the accuracy and speed of disease detection, predict outbreaks more effectively than traditional methods, support vaccination strategies, and optimize healthcare resource allocation. At the same time, it discusses important barriers to implementation, including limited data quality, infrastructure constraints, ethical concerns, and shortages of technical expertise. Overall, the paper highlights AI’s strong potential to strengthen disease surveillance and health outcomes in Africa while emphasizing the need for careful integration, improved data systems, and supportive policy frameworks.