This study introduces a novel, integrated approach for breast cancer diagnosis, addressing one of the most critical challenges in medical sciences: the lack of timely and precise detection. Breast cancer remains a leading cause of mortality worldwide, and early diagnosis plays a pivotal role in improving survival rates. Currently, diagnostic practices heavily rely on physicians' expertise, supported by complex and time-consuming laboratory tests, which are prone to human error and often lead to delays in treatment. To overcome these limitations, this research proposes a comprehensive methodology that combines principal component analysis (PCA) for dimensionality reduction, decision trees for feature selection, and artificial neural networks (ANNs) for classification and prediction. By integrating these techniques, the proposed system optimizes the use of database features, offering an adaptable, efficient, and accurate solution for breast cancer detection. The results demonstrate that this method achieves superior diagnostic accuracy compared to conventional techniques and existing artificial intelligence-based methods referenced in related studies. Furthermore, the system significantly reduces diagnostic costs and time without compromising performance. This research highlights the potential of combining machine learning and data mining techniques to enhance diagnostic precision, providing researchers and clinicians with an effective tool for improving early detection, treatment planning, and patient outcomes.