With the continuous advancement of modern warfare, the complexity of the battlefield environment is progressively increasing, making the importance and challenges of airborne target intent recognition more prominent. Accurate and rapid target intent recognition has become a critical factor in assessing battlefield situations. However, existing literature lacks systematic organization and summarization, which hinders further research and development. Based on this status quo, this paper aims to provide a comprehensive and cutting-edge review to promote the progress of related research. Firstly, this paper organizes the basic concepts of air target intent recognition and its application scenarios, and systematically describes the basic processes and application methods followed in researching this field. Further, this paper systematically reviews the existing research results and analyzes and compares different approaches from four dimensions: probabilistic reasoning, cognitive reasoning, machine learning, and deep learning. Meanwhile, this paper also pays special attention to the development trend of the two emerging fields of UAV intent recognition and cluster intent recognition, and summarizes the current research directions and ideas. Finally, this paper provides a detailed comparative analysis of the above methods, reveals the advantages and limitations of each method, and on the basis of this, puts forward a prospect for the future research direction.