Cervical cancer is a major public health challenge. Further mitigation of cervical cancer can greatly benefit from development of innovative and disruptive technologies for its rapid screening and early detection. The primary objective of this study is to contribute to this aim through large scale screening by development of Artificial Intelligence enabled Intelligent Systems as they can support human cancer experts in making more precise and timely diagnosis. Our current study is focused on development of a robust and interactive algorithm for analysis of colposcope-derived images analysis and a diagnostic tool/scale namely the OM- The Onco-Meter. This tool was trained and tested on 300 In-dian subjects/patients yielding 77% accuracy with a sensitivity of 83.56% and a specicity of 59.25%. OM-The Oncometer is capable of classifying cervigrams into cervical dysplasia, carcinoma in situ (CIS) and invasive cancer(IC). Pro- gramming language - R has been used to implement and compute earth mover distances (EMD) to characterize different diseases labels associated with cervical cancer, computationally. Deployment of automated tools will facilitate early diagnosis in a noninvasive manner leading to a timely clinical intervention for cervical cancer patients upon detection at a Primary Health Care (PHC). The tool developed in this study will aid clinicians to design timely intervention strategies aimed at improving the clinical prognosis of patients.