<p>Medical image analysis constitutes a pivotal domain within contemporary technology. Diverse technical approaches are extensively tested, developed, and employed as instruments for medical diagnosis and prognosis. Cancer is a very prevalent and alarming disease that is on the rise. Cervical cancer ranks as the second most prevalent malignancy among women worldwide. The early detection of this malignancy undoubtedly aids medical practitioners in enhancing diagnosis and treatment. The objective of this study is to develop an efficient biomedical imaging system for the early detection of cervical cancer. A colposcopic image of a patient's cervix is inputted into the system. The noise components of an image are substituted by our system, which was developed in a different workflow. Various sets of texture features are derived from this pre-processed image. The clinical data of the same patient, comprising two tests and laboratory reports, is amalgamated with texture feature sets. Approximately 28 machine learning methods, classified into five principal categories, are utilized on both individual feature sets and combined sets of textural data and clinical reports to evaluate the early prediction of cervical cancer. The suggested hybrid method achieved a significant performance enhancement of 22.3% and 13.2% for two dataset groups, relative to the exclusive use of statistical analysis. The proposed strategy will certainly offer significant insights to medical professionals for the early prediction of cervical cancer.</p>