Cervical cancer is a significant public health issue globally, particularly in regions with limited healthcare resources. Early detection is crucial for effective treatment and improved patient outcomes. This study proposes an automatic classification system for cervical cell images using deep learning methods. The Pap test, a common method for identifying cancer cells, involves a time-consuming manual analysis that is prone to human error. To address this, we developed a deep learning model trained on a dataset of over 5000 cervical cell images obtained through extensive screening processes. The model employs convolutional neural networks (CNNs) to classify images into various stages of cervical cancer and normal cell types. We incorporated techniques such as color conversion, segmentation, and feature extraction to enhance the model’s accuracy. The model was implemented and trained using TensorFlow and TFLearn libraries. Experimental results demonstrate the system’s effectiveness, achieving 98% accuracy in distinguishing between different stages of cervical cancer and normal cell types. The model also showed robustness in handling grayscale and thresholded images, increasing its practical applicability. By facilitating early detection and intervention, this system can significantly reduce the burden of cervical cancer and improve patient care worldwide.