Breast cancer remains one of the most serious health concerns affecting women worldwide. Early detection of malignant tumors significantly improves survival rates and enhances patients' quality of life. As a result, the development of accurate and efficient diagnostic systems for breast cancer is of critical importance. Artificial neural networks (ANNs) have been widely applied in medical diagnosis, particularly in data classification tasks. The backpropagation algorithm is a commonly used technique for training neural networks; however, it suffers from certain limitations, such as slow convergence, susceptibility to local minima, and sensitivity to initial weight selection. To address these challenges, this study employs a data-shuffling ensemble method to improve classification accuracy. The effectiveness of both approaches is assessed in distinguishing between benign and malignant breast tumors using a well-established dataset. Experimental results indicate that the neural network utilizing the data-shuffling ensemble method achieves an accuracy of 99.3%, outperforming the backpropagation algorithm. These findings highlight the potential of ensemble learning techniques in enhancing diagnostic accuracy and reliability in medical applications. The study contributes to the ongoing advancements in artificial intelligence-based diagnostic tools, emphasizing the importance of robust classification techniques in improving breast cancer detection and patient outcomes.