The elevated uterine cancer mortality age is a result of women's ignorance of the value of early detection. Cervical cancer is a serious malignancy that poses a threat to the health of women everywhere, and it has rather subtle early-warning symptoms. It causes harm to the cervix's deep tissues and can eventually spread to other parts of the body like the lungs, liver, and vagina, which can make the situation more challenging. As a result, this study offers a clever method for cervical cancer prediction using DL (Deep Learning) algorithms. We evaluated the performance of our DL models using some different metrics, including recall, precision, and accuracy. The DBN algorithm yields the highest classification score of 92-% in terms of predicting cervical cancer. With MLP, however, accuracy has been determined to be 89%. To evaluate the effectiveness of the models, the computational complexity of traditional DL techniques is calculated.