Because breast cancer affects people everywhere, detecting it early and precisely is very important for patients’ well-being. The goal of this study was to develop and evaluate a machine learning algorithm that can be used to better detect breast cancer. Our model was built using the publicly available “Breast Cancer Wisconsin Dataset” [4]. We conducted tests to compare the results of Logistic Regression, Support Vector Machine (SVM), Neural Networks, Random Forest, Naïve Bayes, and XGBoost since there are several elements to think about while making a medical diagnosis. To enhance the outcomes, we made sure the data was free from any missing information and the features were given an appropriate scale. To reduce the complexity, we use feature selection. We checked how each model performed by reviewing F1-score, precision, recall and accuracy. In order to stop overfitting, models are regularly tested by cross-validating and reviewing their learning curves. With an F1-score of 95.71% and an accuracy of 95.6%, our results demonstrated that the Neural Network model performed remarkably well, indicating that it may be highly beneficial in a clinical context. Using machine learning for healthcare prediction requires careful model selection and data preparation, as emphasized in this work. We also recognize the importance of protecting patient data privacy and security. We conclude that machine learning might significantly improve breast cancer diagnosis in its early stages, providing clinicians with a useful tool to aid patients and improving patient care overall.