 This research proposes the use of four classical machine learning, ML, techniques to create real-time and reliable digital twins of breast cancer for diagnostic and therapeutic purposes. These techniques include support vector machines, SVMs, k-nearest neighbors, KNNs, decision trees, DTs, and random forests, RFs. The study focuses on breast cancer, the second most common type of cancer worldwide. The proposed framework illustrates the process of creating digital twins of breast cancer, and demonstrates their feasibility and reliability in monitoring, diagnosis, and prediction of medical parameters. This article was authored by Olmed Mustarsity, Mohamed, the dad, Jamshidi, Salisargal Zi, and others.