In this paper, the researchers have empirically used the various Machine Learning (ML) and Deep Learning (DL) Algorithms and analyze the findings of different Machine Learning and Deep Learning algorithms on the very well-known Wisconsin Diagnostic Breast Cancer Data-Set (WDBC). This study assessed the degree of their capacity to accurately order the sample images as "malignant" or "benign". The separate utilizing of these algorithms was decided on the grounds of different assessment measurements as accuracy is the main factors of datasets. From the trial results, we rational that the deep learning approaches have given preferable outcomes on assessment grounds over the machine learning algorithms. In quantitative terms, CNN performed most reliably among every one of the considered methodologies for the given breast cancer dataset with an accuracy of CNN Deep Learning Model is 99.48 % and MLP 99.45% individually. The ML algorithm SVM has the betters testing accuracy 97.13% and 98.36% training accuracy. In the consequences of finding breast cancer can be predicted on early basis using the Machine Learning and or Deep Learning Models effectively and efficiently. Early detection of breast cancer (BC) will treat well and save many breast cancer patients. As a result, the BC patient’s rate and death rate can be reduced.