Artificial intelligence (AI) is reshaping cervical cancer screening by automating interpretation of cytology, colposcopic, and related imaging to improve early detection, especially in low- and middle-income countries. This review synthesizes advances in preprocessing; segmentation; representation learning; and supervised, semi-supervised, unsupervised, and transformer-based models, with emphasis on multimodal fusion with HPV testing, spectroscopy, and MRI. Across retrospective datasets and growing real-world deployments, AI systems can achieve high accuracy and sensitivity, accelerate workflows, reduce costs, and expand coverage via portable and edge-computing devices. However, translation is constrained by data bias, variable image quality, opaque decision-making, and fragmented regulation. We outline requirements for clinically robust and equitable deployment, including diverse multi-center datasets, federated and privacy-preserving learning, explainable interfaces, standardized validation with histopathologic endpoints, and clinician-in-the-loop workflows. Finally, we highlight future directions such as hybrid explainable AI with large language models, multi-omics integration, and adaptive models resilient to data drift.