One of the biggest causes of cancer-related fatalities among women is still Cervical cancer, especially in low and middle-income nations where access to broad screening and early detection may be limited. Cervical cancer is curable if detected in its early stages, but asymptomatic progression frequently results in late diagnosis, which makes treatment more difficult and lowers survival chances. Even though they work well, current screening methods including liquid-based cytology and Pap smears have drawbacks in terms of consistency, sensitivity, and specificity. Recent developments in Deep Learning and Artificial Intelligence have shown promise for greatly improving Cervical cancer detection and diagnosis. In this work, we have introduced CervixCan-Net, a novel Deep Learning based model created for the precise classification of Cervical cancer from histopathology images. Our approach offers a solid and dependable classification solution by addressing common problems like overfitting and computational inefficiency. CervixCan-Net performs better than many state-of-the-art models according to a comparison investigation. CervixCan-Net, with an impressive test accuracy of 99.83%, provides a scalable, automated Cervical cancer classification solution that has great promise for improving patient outcomes and diagnostic accuracy.