<p>Airports are crucial hubs in the global transportation. Understanding how air passengers access airports is essential for optimizing airport infrastructure and ensuring seamless operation. This paper reviews the evolution of air passenger mode choice modelling over time, addressing the modelling process, explanatory variables, and the issue of model transferability. Findings reveal a predominant focus on airports located in metropolitan cities, particularly in developed nations. Over time, modelling techniques have evolved from conventional Multinomial Logit (MNL) models to more refined Nested Logit (NL) and Mixed Multinomial Logit (MMNL) models. The study highlights the variables such as trip characteristics, mode-specific attributes, and trip maker’s information in shaping access modes to airports. Future research directions include integrating shared mobility services and leveraging machine learning for more comprehensive models. This can be achieved by incorporating a wider array of explanatory variables, such as real-time traffic data. These advancements will improve airport planning and policymaking.</p>