This paper presents a novel methodology for breast ultrasound image classification using the EfficientNet-B7 model, combined with advanced data augmentation techniques and Explainable AI (XAI). The primary goal is to improve the accuracy and robustness of breast cancer detection, which is a leading cause of mortality among women. Traditional CNN architectures like VGG, ResNet, and DenseNet often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, the study employs EfficientNet-B7, a scalable CNN architecture, and integrates advanced data augmentation techniques such as RandomHorizontalFlip, RandomRotation, and ColorJitter to enhance minority class representation and improve model robustness. The model is fine-tuned on the Breast Ultrasound Images Dataset (BUSI), and early stopping is implemented to prevent overfitting. Additionally, XAI techniques like Grad-CAM are used to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images that influence classification outcomes. The proposed approach achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches. The integration of XAI techniques further enhances the reliability and clinical adoption of the model. This comprehensive framework offers a robust and interpretable tool for early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.This paper presents a novel methodology for breast ultrasound image classification using the EfficientNet-B7 model, combined with advanced data augmentation techniques and Explainable AI (XAI). The primary goal is to improve the accuracy and robustness of breast cancer detection, which is a leading cause of mortality among women. Traditional CNN architectures like VGG, ResNet, and DenseNet often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, the study employs EfficientNet-B7, a scalable CNN architecture, and integrates advanced data augmentation techniques such as RandomHorizontalFlip, RandomRotation, and ColorJitter to enhance minority class representation and improve model robustness. The model is fine-tuned on the Breast Ultrasound Images Dataset (BUSI), and early stopping is implemented to prevent overfitting. Additionally, XAI techniques like Grad-CAM are used to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images that influence classification outcomes. The proposed approach achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches. The integration of XAI techniques further enhances the reliability and clinical adoption of the model. This comprehensive framework offers a robust and interpretable tool for early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.