Papaya leaf diseases are serious threats to papaya production globally and their identification and classification at an early stage are extremely important for disease control. Conventional approaches of disease diagnosis are normally inconclusive and very slow hence the need for techniques that can support the diagnosis process. This review seeks to detail the discovery of the several main AI methodologies especially ML and DL models for papaya leaf disease diagnosis. The objectives are to define the drawbacks of modern approaches to disease detection, comparing the merits and demerits of applying various AI techniques, and outlining potential lines of further investigation. Based on these developments, this review work seeks to provide a synthesis of knowledge and find of how, through the use of AI, the identification of papaya leave diseases can be enhanced in terms of accuracy and timeliness as a way of supporting improved management of the crop.