The plateau environment poses significant challenges to the physiology and operational capability of military personnel, which is also a key contributor to non-combat casualties. Traditional evaluation methods using single-modal indicators lack dynamic monitoring capabilities and fail to capture complex dynamic stress reactions. This paper reviews the research progress in multimodal data-driven evaluation models for plateau adaptability by analyzing the pathological mechanisms and influencing factors of plateau adaptability and outlining the limitations of current standards and single-modal machine learning. The construction of multimodal data-driven models is explored, focusing on both data architectures that combine physiological, behavioral and environmental modalities for military scenarios and cross-modal alignment and fusion techniques. Furthermore, military applications such as dynamic early warning of non-combat casualties, personalized acclimatization and intelligent military health support are described. Finally, future developments in multimodal data-driven evaluation models for plateau adaptability are predicted in hopes of contributing to the combat effectiveness of troops on the plateau.