Globally, breast cancer sticks out to be the most widespread cancer types which is the major factor contributing to women’s mortality. Numerous clinical, social, lifestyle and economic factors have a role in the development of this condition. Thus, this paper will compare and contrast the performances of several classifications such as algorithms, Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM), all for breast cancer prediction. In addition, models are evaluated and compared the efficiency of diverse Machine Learning (ML) classifiers depending on the particular indicators which can be, for instance, accuracy, recall, precision, ROC curves, AUC scores and F1-Score. Breast Cancer’s dataset is applied for this analysis. It was found out from UCI Machine-Learning Repository. Therefore, the datasets are divided into two types in the implementation phase, 80% for training and 20% for testing. The present paper aims to analyze and compare the specified supervised machine-learning algorithms, including DT, RF, SVM and LR, that can be so useful to predict breast cancer based on clinical and biopsy information. All parameters show that SVM works significantly much better than most other ML algorithms.