This study presents a novel approach for breast ultrasound image classification using the EfficientNet-B7 model combined with Explainable AI (XAI) techniques. Breast cancer is a leading cause of mortality among women globally, necessitating accurate classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures like VGG, ResNet, and DenseNet face challenges such as class imbalance and subtle texture variations, leading to reduced accuracy for minority classes like malignant tumors. To address these issues, the study proposes a methodology leveraging EfficientNet-B7, a scalable CNN architecture, with advanced data augmentation techniques to enhance minority class representation and improve model robustness. The model is fine-tuned on the BUSI dataset, incorporating RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. Early stopping is used to prevent overfitting and optimize performance metrics. Additionally, XAI techniques like Grad-CAM are integrated to enhance model interpretability and transparency, providing visual and quantitative insights into the features influencing classification outcomes. The model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches. The integration of XAI techniques enhances understanding of the model's decision-making process, increasing its reliability and facilitating clinical adoption. The framework offers a robust and interpretable tool for early detection and diagnosis of breast cancer, advancing automated diagnostic systems and supporting clinical decision-making. The study also highlights the effectiveness of data augmentation, preprocessing, and XAI in improving model performance and interpretability. The results demonstrate the high accuracy and robustness of the EfficientNet-B7 model in classifying breast ultrasound images, with exceptional precision, recall, and overall accuracy. The model's performance is further validated by error metrics and ROC-AUC scores, indicating its superior ability to distinguish between different classes. The study concludes that integrating scalable CNN architectures with advanced augmentation and XAI techniques can significantly improve automated diagnostic systems for breast cancer. Future work includes expanding the methodology to larger datasets and exploring other state-of-the-art architectures and ensemble learning techniques.This study presents a novel approach for breast ultrasound image classification using the EfficientNet-B7 model combined with Explainable AI (XAI) techniques. Breast cancer is a leading cause of mortality among women globally, necessitating accurate classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures like VGG, ResNet, and DenseNet face challenges such as class imbalance and subtle texture variations, leading to reduced accuracy for minority classes like malignant tumors. To address these issues, the study proposes a methodology leveraging EfficientNet-B7, a scalable CNN architecture, with advanced data augmentation techniques to enhance minority class representation and improve model robustness. The model is fine-tuned on the BUSI dataset, incorporating RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. Early stopping is used to prevent overfitting and optimize performance metrics. Additionally, XAI techniques like Grad-CAM are integrated to enhance model interpretability and transparency, providing visual and quantitative insights into the features influencing classification outcomes. The model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches. The integration of XAI techniques enhances understanding of the model's decision-making process, increasing its reliability and facilitating clinical adoption. The framework offers a robust and interpretable tool for early detection and diagnosis of breast cancer, advancing automated diagnostic systems and supporting clinical decision-making. The study also highlights the effectiveness of data augmentation, preprocessing, and XAI in improving model performance and interpretability. The results demonstrate the high accuracy and robustness of the EfficientNet-B7 model in classifying breast ultrasound images, with exceptional precision, recall, and overall accuracy. The model's performance is further validated by error metrics and ROC-AUC scores, indicating its superior ability to distinguish between different classes. The study concludes that integrating scalable CNN architectures with advanced augmentation and XAI techniques can significantly improve automated diagnostic systems for breast cancer. Future work includes expanding the methodology to larger datasets and exploring other state-of-the-art architectures and ensemble learning techniques.