Breast cancer remains a major global health concern and the most commonly diagnosed cancer among women worldwide, according to the World Health Organization (WHO). This suggests the urgent need for early detection and effective treatment. One of the key challenges in breast cancer diagnosis is the time-intensive process of identifying specific symptoms, such as abnormal masses or irregular tissue structures. These challenges arise due to the complexity and significant variability in how breast cancer manifests among different patients. To address these diagnostic difficulties, this study proposes BreCanLens, an AI-driven image analysis system integrated with mixed reality (MR) technology for breast cancer tracking and diagnostic support . The system utilizes a convolutional neural network (CNN) to analyze medical images, detecting intricate patterns that might be imperceptible to the human eye. CNNs are particularly effective in medical imaging due to their ability to process high-dimensional data and differentiate between benign and malignant breast tissues. This capability enhances diagnostic accuracy and provides healthcare professionals with a reliable reference for clinical decision-making. Additionally, MR technology further improves diagnostic workflows by overlaying imaging data directly onto patients during examinations or by simulating complex procedures through virtual models. This real-time visualization enhances clinical efficiency, reduces the need for repeated consultations, and lowers medical costs. The results demonstrate that BreCanLens effectively enhances diagnostic accuracy, streamlines image analysis, and provides valuable support to healthcare professionals through advanced AI and MR integration.