Cervical cancer (CC) is one of the most common female cancers worldwide. It remains a significant global health challenge, particularly affecting women in diverse regions. The pivotal role of human papillomavirus (HPV) infection in cervical carcinogenesis underscores the critical importance of diagnostic strategies targeting both HPV infection and cervical intraepithelial neoplasia (CIN) for effective cervical cancer prevention. Epidemiological evidence establishes a strong association between persistent high-risk HPV infections, particularly types 16 and 18, and the development of cervical cancer. Therefore, early detection of HPV infection through diagnostic screening emerges as a fundamental preventive measure. The implementation of routine HPV testing as a primary screening tool has proven effective in identifying individuals at heightened risk, allowing for timely intervention and surveillance. CIN, encompassing precancerous changes in the cervical epithelium, serves as a direct precursor to invasive cervical cancer. Accurate diagnosis of CIN is crucial for preventing the progression of lesions to malignancy. Cytological examinations, such as Pap smears, and advanced imaging techniques like colposcopy play indispensable roles in diagnosing CIN, enabling healthcare providers to intervene early. The dual emphasis on HPV infection diagnostics and CIN detection forms the cornerstone of cervical cancer prevention strategies. Early identification and management of HPV-related risks, coupled with precise CIN diagnostics, empower healthcare systems to proactively address the threat of cervical cancer, ultimately reducing its incidence and improving women’s health globally. The evolving landscape of medical technology underscores the need for modern diagnostic methods, also in cervical cancer screening, with an increasingly important role being played by AI-based techniques. This chapter summarizes the characteristic features of different CIN stages. It reviews current and future AI-based techniques for the identification of cervical cancer using machine learning approaches and medical data mining, including automated analysis of pap smears, colposcopy image analysis, deep learning in histopathological analysis, and challenges such as remote screening and telemedicine.