Breast cancer, the most frequently identified condition in women globally as of 2024, is one of the main reasons of death in women, according to IARC. Better therapy and treatment are possible when breast cancer is predicted early. A decent survival rate has been attributed to early identification of this cancer. Innovations in artificial intelligence and machine learning have resulted in the creation of improved and more accurate models for the identification and management of this illness. Knowledge Discovery in Databases (KDD), is widely used data preparation methods by medical researchers to analyze trends and relationships between variables and predict the progression of a disease based on historical data from datasets. The accuracy of each model is necessary to be estimated to choose the best model for cancer prediction. As a result, this paper describes few effective machine learning models that can identify breast cancer from a tabular dataset in varied accuracies. Using five distinct classification models—k-nearest neighbor, decision trees, random forest, SVM and the logistic regression model—the attempt is to identify the best model to detect breast cancer. The results shows that SVM performs well with the highest accuracy of 98.24%. The future goal in machine learning is to deploy models that leverage sophisticated algorithmic and data-driven approaches to improve breast cancer prediction precision accuracy and efficacy. By integrating innovative approaches from existing research, the aim is to facilitate early detection, support informed treatment decisions, and enhance patient outcomes.