Cervical cancer is among the most prevalent malignancies affecting women globally and poses a significant threat to women’s health. Predominantly caused by persistent infection with the Human Papillomavirus (HPV), this cancer can progress silently through asymptomatic stages before becoming life-threatening. A primary diagnostic method for early detection is the Pap smear test, which involves collecting cervical cell samples to detect precancerous changes. However, manual interpretation of Pap smear images is prone to human error and places a considerable burden on healthcare systems. The application of Machine Learning (ML), particularly Convolutional Neural Networks (CNNs), has enabled more efficient and accurate analysis of cytological images. This paper explores the integration of various ML methods to enhance diagnostic accuracy and speed in detecting cervical cancer and investigates the key challenges and prospects in this domain. Specifically, a hybrid approach combining CNN with algorithms such as K-Nearest Neighbors (K-NN), Decision Trees, and Support Vector Machines (SVM) is proposed. The study presents an advanced diagnostic pipeline that incorporates radiomic feature extraction, stain normalization, Principal Component Analysis (PCA) for dimensionality reduction, and ensemble learning strategies. The proposed model demonstrated high performance, achieving 98.3% accuracy on the Herlev dataset and 97.5% on SIPaKMeD, outperforming traditional baseline models.