This paper reviews recent advancements in applying deep learning techniques to cervical cancer detection from medical images. Cervical cancer continues to be a critical public health issue, with high morbidity and mortality rates globally. Despite the availability of established screening methods, many women face barriers to early diagnosis, leading to advanced-stage detection and poor prognoses. Traditional diagnostic approaches are often constrained by subjective interpretation and inconsistent accuracy, necessitating the development of more reliable diagnostic tools. Deep learning has emerged as a powerful tool in medical diagnostics, offering significant improvements in accuracy and efficiency. This review synthesizes the most recent studies that have applied deep learning models, such as CNNs, EfficientNet, YOLO, and others, to enhance cervical cancer screening and diagnosis. It highlights methodological innovations, discusses the integration of AI in clinical workflows, and identifies future research directions, particularly the need for diverse datasets and model interpretability. By showcasing the potential of deep learning to transform cervical cancer diagnostics, this paper seeks to enhance the development of more accessible, precise, and automated diagnostic tools, thereby improving patient outcomes, particularly in underserved regions.