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Breast cancer is one of the most common and life-threatening diseases among women worldwide. Early detection plays a crucial role in improving survival rates. Ultrasound imaging is widely used due to its non-invasive and cost-effective nature, especially for dense breast tissues. This paper proposes an automated breast cancer detection system using deep learning techniques. A Convolutional Neural Network (CNN) model is utilized to classify ultrasound images into benign, malignant, and normal categories. The proposed system reduces human error, improves diagnostic accuracy, and provides faster results. Experimental results demonstrate that the model achieves high accuracy, precision, and recall, making it a reliable tool for medical diagnosis.