Abstract highlights the importance of deep learning (DL) technologies in cervical cancer (CC), which is the leading cause of death among women in the world. With more than 700 deaths per day and 400,000 deaths per year expected by 2030, early diagnosis is essential. The use of DL techniques leads to more accurate diagnosis, which enhances treatment outcomes. To facilitate the building of reliable classification models such as SVM, KNN, Bayesian Networks, Decision Trees, MLP, the research includes several deep learning models such as CNN, DenseNet, Xception feature extraction and also explores YoloV5 and YoloV8-based DL-based detection methods for CC separation. The use of these models greatly improves diagnostic accuracy. In the initial analysis, CNN and SVM achieved 99% accuracy. Extending the program using YoloV5 and YoloV8 for detection services further improves performance and improves the system’s ability to correctly detect CC This analysis has implications beyond accurate diagnosis; by reducing morbidity and mortality, it will benefit women everywhere, especially in low-income countries. With effective diagnostic tools, health care providers can better serve their patients by providing personalized treatment and early intervention. All things considered, the trial highlights the importance of DL technology in preventing CC and improving patient outcomes.