The goal of this work is to apply Convolutional Neural Network (CNN) techniques to produce a reliable user interface (UI) for cervical cancer remediation and prediction. The cervicalcellclassesthatthe proposedapproachisintended to identify properly are Dyskeratotic, Koilocytotic,Metaplastic, Parabasal and Superficial-Intermediate. Using CNN's capabilities, the model achieves a remarkable 98% accuracy rate, demonstrating its effectiveness in categorizing various cervical cell types. The user interface (UI) emphasizesa complete approach to cervical health by not only predicting the existence of cervical cancer but also making remediation techniques easier. This ground-breaking method has the potential to significantly lessen the burden of cervical cancer, which is a common and sometimes fatal illness, by facilitating early identification and intervention.