Breast cancer poses a crucial alarming health risk to individuals. Radiological diagnosis relies deeply on visual evaluation by radiologists. However, the detection of this malignancy is a challenge due to the changes displayed in histopathological images. Deep learning (DL) keeps getting better at health care and radiodiagnosis. This study improves the accuracy and usefulness of deep learning techniques in order to diagnose breast cancer. This is done through changing EfficientNet architectures’ performance from B0 to B7. The dataset covered breast histopathology images, imposing preprocessing such as image resizing to adapt to DL model input prerequisites. Subsequently, transfer learning smoothed network tuning to enhance diagnostic performance. This study defines mechanism that improves precision in diagnosing breast cancer through deep learning by making EfficientNet mechanism B0–B7 better. EfficientNet B7 achieves 95.66% accuracy. This advances the diagnosis accuracy and makes it more useful.