Breast cancer is the primary form of cancer affecting women and manual diagnostic of the disease is time-consuming and limited by the availability of screening systems. Thus, an urgent requirement exists for an automated diagnosis system capable of swiftly detecting cancer at its early stages. Artificial intelligence techniques have emerged as a valuable tool for developing such a system. In this investigation, we assessed the performance of various machine learning models using the Wisconsin Breast Cancer (original) dataset, with a particular focus on identifying the most effective models for breast cancer diagnosis. The study’s results align with previous research, underscoring the effectiveness of Support Vector Machines (SVM), Decision Trees, Classification and Regression Trees (CART), Artificial Neural Networks (ANN), and Extreme Learning Machine Artificial Neural Networks (ELM ANN) in breast cancer detection. Several models, including SVM, Random Forest Classifier, Decision Tree, Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree (again), AdaBoost, Gradient Boost, XGBoost, LGBM Classifier, and Custom Classifier, demonstrated high accuracy, precision, and F1 scores for distinguishing between benign and malignant tumors, as indicated by the study’s findings. The research highlights the significance of hyperparameter tuning and the effectiveness of boosting algorithms in addressing the complexity and non-linearity of the data.