Cervical cancer continues to be a significant cause of cancer-related deaths among women, especially in low-resource settings where screening and follow-up care are lacking. The transcription factor zinc finger E-box-binding homeobox 2 (ZEB2) has been identified as a potential marker for tumour aggressiveness and cancer progression in cervical cancer tissues. This study presents a hybrid deep learning system developed to classify cervical cancer images based on ZEB2 expression. The system integrates multiple convolutional neural network models-EfficientNet, DenseNet, and InceptionNet-using ensemble voting. We utilised the gradient-weighted class activation mapping (Grad-CAM) visualisation technique to improve the interpretability of the decisions made by the convolutional neural networks. The dataset consisted of 649 annotated images, which were divided into training, validation, and testing sets. The hybrid model exhibited a high classification accuracy of 94.4% on the test set. The Grad-CAM visualisations offered insights into the model's decision-making process, emphasising the image regions crucial for classifying ZEB2 expression levels. The proposed hybrid deep learning model presents an effective and interpretable method for the classification of cervical cancer based on ZEB2 expression. This approach holds the potential to substantially aid in early diagnosis, thereby potentially enhancing patient outcomes and mitigating healthcare costs. Future endeavours will concentrate on enhancing the model's accuracy and investigating its applicability to other cancer types.