Cervical cancer is an important cause of deaths secondary to cancer in women throughout the world, where a timely diagnosis is needed in order to improve the outcome. Traditional diagnostic techniques based on Pap smear test as well as histopathology assessments are often hampered by limitations such as operator dependency and time-consuming processes. This review provides a comprehensive understanding of the most recent advancements in cervical cancer detection through use of Machine Learning and Deep Learning techniques. This paper critically analyzes the classical algorithms, including SVMs and Random Forests, together with modern models like CNNs, ViTs, and hybrid architectures. Moreover, it evaluates sophisticated methods, including transfer learning, ensemble learning, and data augmentation, to examine whether they are effective for improving classification accuracy and robustness. In particular, practical aspects are paid more attention through real-time diagnostic systems that are IoT-driven frameworks appropriate for use in low-resource environments to public datasets like Herlev and SIPaKMeD. Important findings indicate that ensemble techniques, which include fuzzy ranking and weighted voting, enhance the accuracy of prediction, achieving performance greater than 89% in various studies. Also, multi-task learning, label smoothing, and data augmentation by generative adversarial networks address problems arising from class imbalance and noisy labeling. Also, the paper discusses some significant research gaps, including explainable artificial intelligence models and federated learning, in order to enable privacy-preserving use of data. This review emphasizes the transformative potential of DL in cervical cancer detection by synthesizing existing literature and proposing future research directions. The findings are expected to inspire further innovation, making early detection more accurate, accessible, and reliable, especially in underserved healthcare environments.