Breast cancer is a serious health issue for women everywhere, and effective treatment depends on early identification. For medical imaging, automated breast cancer identification is essential to speeding up diagnosis; however, current approaches frequently have extensive analysis durations. In order to address this, we investigate the usage of neural networks (NN) and concentrate on the backpropagation technique in particular. We found deficiencies in the sensitivity and specificity of the existing NN models by conducting a thorough literature analysis. Our method uses backpropagation to train a neural network (NN) for effective categorization of benign and malignant patterns in breast cancer images. Preliminary findings demonstrate decreased processing time and increased accuracy. A comparative study with current methods demonstrates our method’s effectiveness. To improve diagnostic precision even more, future improvements might include advanced NN architectures.