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Simple Summary: Current ultrasound imaging methods for detecting breast cancer often face challenges with image quality, making it tough to accurately spot tumors. This paper introduces UCapsNet, a new model that combines two advanced techniques to enhance breast cancer detection in ultrasound images. By improving how tumors are segmented and classified, UCapsNet aims to deliver clearer and more accurate results compared to traditional methods. Our findings indicate that UCapsNet can greatly improve diagnostic precision, enabling earlier detection and better treatment options for patients. This research could significantly impact future studies and lead to improved breast cancer detection practices in the medical community. Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net's powerful segmentation capabilities with the capsule network's high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings. [ABSTRACT FROM AUTHOR]