Breast cancer remains one of the fundamental reasons of dying for women generally, which highlights the significance of early and specific detection. In this work, histopathological images from the BreakHis dataset are used to categorize breast cancer the use of a transfer learning-based method. To extract deep characteristics and categorize tumor images into benign and malignant groups, pre-trained Convolutional Neural Networks (CNNs) such ResNet50, VGG16, and InceptionV3 have been refined. Effective training at the relatively small clinical dataset is made feasible through the usage of transfer learning, which capitalizes on beyond information from considerable image datasets. To determine model performance, a whole lot of tests have been performed at diverse magnification settings (40x, 100x, 200x, and 400x). The outcomes display that the counseled technique outdoes conventional training from scratch, accomplishing usually high accuracy, sensitivity, and specificity. This examine demonstrates the efficacy of deep transfer learning in the analysis of breast cancer. It has remarkable promise to be used in pathology’s real-time clinical decision support systems.