Breast cancer affects millions of people globally, providing a widespread and catastrophic health risk. Even though early identification of this illness is crucial for improving patient outcomes, it is difficult to identify and manage appropriately. Artificial intelligence (AI) has the potential to revolutionize breast cancer treatment. In this paper, a novel hybrid ML model known as HAXM is introduced. The major goal is to evaluate each algorithm's usefulness and performance using several measures such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC)-ROC curve. In this examination, special emphasis is placed on the accuracy of data categorization. The trial results clearly show that HAXM stands out, with the greatest accuracy rate of 99.41% and the lowest error rate. AI has the potential to effect a paradigm shift that significantly improves outcomes in breast cancer. This is accomplished by allowing for early identification, personalizing medicines to specific patients, and improving clinical decision-making procedures.