Cervical cancer is the fourth most common cancer among women, especially in developing countries, and early detection is vital for effective treatment and improved survival. Several machine learning methods have been used for diagnosis; however, a systematic review is needed to understand progress in predictive outcomes and the challenges of applying computer-aided diagnosis in healthcare settings. This review, conducted using PRISMA guidelines, examines studies on machine learning and deep learning techniques used for cervical cancer detection, with the goal of identifying their strengths and weaknesses to support future research and practical use in healthcare. Peer-reviewed articles were selected from Google Scholar and PubMed using search terms such as “cervical cancer,” “machine learning,” “deep learning,” “classification,” “prediction,” and “detection,” and studies were included or excluded based on predefined criteria. Forty-six studies met the inclusion criteria, of which 18 focused on deep learning. Deep learning models achieved accuracy, AUC, and F1 scores ranging from 77% to 99%, consistently outperforming other machine learning methods, which had scores between 61% and 99%. Overall, deep learning methods have demonstrated higher accuracy in detecting cervical cancer; however, future studies should validate these models using larger and more diverse datasets to improve reliability and clinical relevance.