Purpose <p>Cervical cancer remains a major global threat to women’s health, and accurate screening, diagnosis, and individualized care increasingly depend on the integration of multimodal clinical information. This review aims to synthesize current convolutional neural network (CNN)-based methods for multimodal analysis in cervical cancer, with particular focus on how CNN-driven multimodal learning integrates imaging, pathological, molecular, and clinical data to improve cervical cancer diagnosis, prognosis prediction, and clinical decision support.</p> Methods <p>We reviewed common cervical cancer-related data modalities, including imaging, pathology, molecular data, and clinical variables, and summarized their characteristics that influence preprocessing strategies and network design. We further examined CNN-based approaches for modality-specific analysis, multimodal fusion strategies, and hybrid architectures integrating CNNs with attention mechanisms, Transformers, or graph models.</p> Results <p>Existing studies indicate that CNN-driven multimodal learning can improve sensitivity, diagnostic accuracy, and prognostic performance compared with unimodal approaches. Multimodal fusion enables complementary integration of imaging, pathological, molecular, and clinical information, supporting predictive tasks in diagnosis and prognosis. However, current methods still face challenges such as inter-modal correspondence modeling, data imbalance, limited interpretability, missing modalities, cross-center domain shifts, and privacy constraints.</p> Conclusion <p>CNN-based multimodal analysis shows considerable potential for advancing precision gynecologic oncology. Future directions include Transformer architectures and foundation models, privacy-preserving federated learning, self-supervised pretraining, and personalized clinical decision-support systems to promote clinical translation.</p>