Treatment results and patient survival rates are greatly enhanced by early and accurate identification of breast cancer. Convolutional neural networks (CNNs), a recent development in deep learning, have shown incredible promise for medical picture interpretation. The research proposes a DenseNet-driven framework capable of multi-level feature representation for early diagnosis of breast cancer based on mammography, ultrasonography and histopathology medical imaging modalities. Using the unique dense connectivity pattern inherent in DenseNet to enhance gradient flow and facilitate feature reuse, we have effectively developed a way to extract high level semantic information and low level structural features from breast cancer related images across multiple levels of a DenseNet. By systematically fusing high-and-loevel features from all levels of the DenseNet architecture, the proposed framework produces superior discriminative capacity and greater robustness to tumor size, shape and texture variation than previous methods. The proposed framework employs optimized preprocessing, data augmentation, and class imbalance techniques to enhance generalization performance. A detailed analysis of diagnostic accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) reveals that the proposed model outperforms state-of-the-art descriptors and existing CNN-based approaches on benchmark breast cancer imaging datasets. The results indicate that multi-level feature learning using DenseNet provides reliable and effective means for the early diagnosis of breast cancer, providing an opportunity for further incorporation into CAD systems to support clinicians’ decision making.