Early identification greatly enhances treatment outcomes and survival rates for breast cancer, which remains a major global health concern. Breast cancer is a major health concern around the world, and early detection is critical for better patient outcomes. In this study, we employ a dataset comprising several clinical and demographic data to investigate the use of machine learning algorithms for breast cancer prediction. This study aims to develop a prediction model that, using a collection of pertinent parameters, can reliably categorize cases of breast cancer as benign or malignant. The goal is to turn out reliable breast cancer detection tool that aids in patient diagnosis by utilizing past data and machine learning techniques. A thorough evaluation of Machine Learning (ML) methods for breast cancer prediction is provided in this research paper. The main goal is to create a prediction model that may help medical practitioners with early diagnosis and individualized treatment planning. It should be accurate and dependable. In addition, Mean absolute error (MAE) and mean squared error (MSE) are useful measures for determining how accurately a model predicts real-world events. These measures evaluate a model's accuracy and potential use in practical scenarios. This research contributes to the continuing attempts to leverage technology for personalized healthcare and early illness diagnosis. The developed machine learning models will ultimately improve patient outcomes and reduce the death rate from breast cancer by assisting medical professionals in making educated decisions about screening and therapeutic strategies.