Cervical cancer is a major threat to women's health. Multimodal fusion AI models integrate cytological, histopathological, clinical, and molecular data, potentially improving detection sensitivity, grading accuracy, and screening efficiency. Traditional methods face limitations in subjectivity, efficiency, and primary care resources. This review details the technical architecture, fusion strategies (deep learning, attention mechanisms, cross‑modal alignment), clinical applications, and translational challenges of multimodal fusion AI models. With further refinement and validation, these models may become useful tools for improving cervical cancer screening and prevention.
