Breast cancer remains among the leading causes of female deaths worldwide thus demanding highly accurate early diagnostic tools. The proposed research adopts deep learning algorithms which unite Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to achieve better breast cancer detection performance from medical imaging records. Traditional machine learning approaches including Decision Trees (DT) and Support Vector Machines (SVM) struggle to identify complex patterns with spatial dependencies in medical images even though they are commonly utilized for classification purposes. This application benefits from the CNN-RNN hybrid model because each network architecture extracts different features while RNNs excellently track sequential dependencies in addition to CNNs' ability to capture intricate spatial features. Test results using benchmark breast cancer datasets show that the proposed CNN-RNN method achieves superior performance than both DT and SVM classifiers according to accuracy and precision and recall and F1-score measurements. This combination approach decreases misdiagnosis occurrences while making the classification process more reliable. The study demonstrates how deep learning produces results better than traditional methods in medical diagnostics and it presents an attractive detection system suitable for early breast cancer screening which can be added to clinical support tools in which the proposed classifier gave best accuracy i.e., 94% when compared with other 2 classifiers.