<p>One of the most common forms of cancer, breast cancer, is difficult to identify in its early stages because of its slower growth. Late detection of this disease in fatal stages leads to high mortality rates. The rise in the number of cases every year has put an enormous strain on the available number of medical experts for diagnosing the disease. Existing methods, such as histology (biopsy), are the most effective way to confirm the presence of breast cancer, but are considered a relatively slower approach. This work introduces Patho-AI, an innovative Computer-Aided Diagnosis (CAD) system that uses deep convolutional neural networks (CNNs) to identify and classify breast cancer using histopathological images. Patho-AI seeks to improve the accuracy and transparency of cancer detection by incorporating Explainable AI (XAI) approaches, thus addressing the issues that pathologists experience while evaluating images manually. We have employed several robust deep learning architectures such as VGG16, VGG19, ResNet50, EfficientNetV2 Small, and ConvNeXtTiny since these models are the top picks among researchers studying deep learning-based medical image analysis. All the models were trained in three modes: Fine Tuning, Transfer Learning, and Scratch Learning to achieve unbiasedness. Various XAI technologies, such as Grad-CAM, LIME, and Integrated Gradient (IG), are applied to provide enhanced insight into model decisions, thus raising user trust and confidence in the proposed system. Data augmentation is performed to achieve better model generalization and convergence. Almost all of the models performed admirably in terms of accuracy, loss, precision, recall, and F1-Score during the fine-tuning mode, with an accuracy of 99% or more. The implementation of these deep learning models, together with effective data augmentation and XAI, yielded a highly accurate system showing transparent outputs for breast cancer detection, which will further assist clinicians in gaining better trust and comprehension of the model’s predictions. Patho-AI marks a substantial development in applying deep learning to diagnose breast cancer using histopathology images. By providing a straightforward and accurate diagnostic application, it tackles the constraints of manual evaluation and improves diagnostic efficiency. The proposed expert system reduces the chances of false positive and negative evaluations, which are common in manual evaluation.</p>