Cervical cancer is one of the main cause of cancer death, impacting 570,000 people globally. Cervical cancer is caused by the Human Papillomavirus (HPV), which causes abnormal cell growth in the cervical region. Periodic HPV testing in woman has helped to minimize the death rate in developed countries. However, due to a shortage of affordable medical facilities, developing countries are still striving to deliver low-cost solutions. The most commonly used screening test for early detection of abnormal cells and cancer is the Pap smear test. This paper explores existing deep learning model and recent research works done using publicly accessible Intel and Mobile-ODT Kaggle dataset for cervix detection and classification and ensures that these automated technologies helps pathologist in providing fast and cost-effective results.