Breast cancer continues to be the primary cause of death and the most prevalent cancer in women that poses a severe risk of death. The control of cancer progression largely depends on early identification; in this regard, machine learning (ML) techniques have become essential tools for cancer prediction and detection. In this study, it examines numerous machine learning models that use various categorization techniques to predict breast cancer. A correlation matrix is one of the organized processes that are part of the technique. In this research paper, we have used Pearson correlation and other structured processes including data preparation, transformation, and collection. We also use a genetic algorithm to determine which attributes are most significant. The algorithms Random Forest (RF), Neural Network (NN), Support Vector Machine (SVM), Gradient Boosting (GB), and Neural Network (NN) are assessed for performance. The random forest model achieved the highest accuracy at 90.07% for breast cancer prediction.