Background: Emerging infectious diseases (EIDs) pose significant challenges to public health systems globally. They are diseases that are caused by an infectious pathogen that has evolved in the population in the past two decades, changed pathogenesis, or increased in incidence, geographic, impact, host, or vector range. Most EIDs are of animal origin. Climate change and increased international travel and trade facilitate the rapid spread of infectious agents across borders causing epidemics. Control and prevention strategies of EIDs are complicated and stressful as they require a One Health approach which involves collaboration between human, animal, environmental, and other disciplines. Modelling and prediction of EIDs outbreaks had gained attention due to the advance in big data and machine learning. Modelling is classified into broad categories; compartmental models which divide the population into compartments based on the disease status, agent-based models that simulate the behavior of individual agents, network models which represent interactions between individuals as a network (nodes and edges), and spatial models which incorporate geographic information to study how diseases spread across physical space. Conclusion: Each modelling technique has strengths and limitations, and the choice of model depends on the specific policymaker questions, available data, and computational resources. Integrated approaches combining multiple techniques provide more comprehensive insights into infectious disease dynamics.