Cancer has manifested itself as one of the most serious health problems facing humanity 9 today. Cervical cancer is one of the most common types of cancer, and the Papanicolaou (Pap) 10 test, often known as a Pap smear, is the most basic test for cervical screening. It involves the 11 microscopic inspection of cervical cells obtained from the cervix, and it is performed by a 12 physician. In order to achieve this, automated detection and classification of cervical cancer 13 from pap-smear images has become a must just because it allows for accurate, reliable, and 14 rapid investigation of the condition's progression. With an emphasis on the history of pap 15 screening, liquid based cytology, and machine learning for cervical cancer detection in recent 16 research publications, this paper provides a summary of the state of the art as stated in several 17 significant recent information sources. For the first time, an evaluation of image analysis and 18 machine learning applications in the growing trends of cervical cancer diagnosis from pap19 smear images over the course of a decade has been published. The survey examines 26 journal 20 papers that were obtained electronically through major scientific databases such as PubMed, 21 Google Scholar, Scopus, IEEE, and Science Direct, which were searched using sets of 22 keywords. The papers were obtained from major scientific databases such as Pubmed, Google 23 Scholar, Scopus, IEEE, and Science Direct. Whenever the Pap test is improved through the 24 use of artificial intelligence, the sensitivity for the detection of cervical pathology is improved 25 as well. The general public should be taught about the Pap smear test with AI, including its 26 purpose and the frequency with which it must be performed, through comprehensive 27 programmes aimed at improving disease management in general.