This study introduces DAtt-ResUNet++, an advanced deep learning model specifically designed to enhance breast tumor segmentation in ultrasound images. The model integrates a Dual-Attention mechanism within the ResUNet++ framework, significantly improving its ability to focus on tumor regions while capturing relevant contextual information from surrounding tissue. By combining both spatial and channel-based attention, DAtt-ResUNet++ achieves higher segmentation precision. For evaluation, the model was rigorously tested on a public dataset of 780 breast ultrasound images, utilizing a robust 10-fold cross-validation approach. It achieved impressive results with a Dice similarity coefficient of 90.40 ± 0.88%, Intersection over Union (IoU) of 84.62 ± 1.12%, Sensitivity of 89.82 ± 0.75%, Precision of 93.44 ± 0.56%, and Accuracy of 98.73 ± 0.12%. These results position DAtt-ResUNet++ as a competitive tool against state-of-the-art methods, showcasing its potential to improve breast tumor segmentation in ultrasound imaging. Future research will explore further optimizations and validation on additional datasets.