Zoonotic diseases cause 60% of total infectious diseases and 75% of emerging infections, and are a core danger to global health security. Climate change, globalization, and urbanization are also responsible for accelerating the convergence of determinants between people, animals, and the environment, thus driving the probability of spillover events. A One Health interdisciplinary approach is essential for building sustainable disease prevention and control against such multifactorial threats. To achieve that, artificial intelligence has been a key technology that is revolutionizing zoonotic and public health early disease detection, surveillance, diagnosis, and prediction modeling. AI enables faster epidemic forecasting, better resource allocation, and gene tracking using technologies such as computer vision, machine learning, deep learning, and natural language processing. AI is also employing predictive statistics and bioinformatics to support drug and vaccine discovery. AI in human, animal, and environmental health systems holds exceptional promise for enhancing health equity and epidemic readiness to counter threats such as data privacy, algorithmic bias, and infrastructure inequality. The objective of this review is to critically evaluate the growing contribution of AI in the fight against zoonotic and public health diseases, examine the ways in which it can be incorporated into the One Health model, and outline directions for developing morally acceptable, transparent, and sustainable AI-based healthcare systems.

To Cite This Article: Almuzaini AM and Alajaji AI, 2025. The role of artificial intelligence in combating zoonotic and public health infectious diseases: a one health perspective on challenges and future directions. Pak Vet J, 45(4): 1532-1542. http://dx.doi.org/10.29261/pakvetj/2025.1036