Abnormal growth of breast cells causes breast cancer. These cells divide faster than healthy cells and continue to accumulate, forming a lump or mass. The cells can spread through the breast to the lymph nodes or other body parts. But fortunately, it is also curable cancer in its early stages. Breast cancer is among the 20 leading causes of death worldwide, affecting approximately 10% of the world's female population. As the number of people with breast cancer increases, effective predictive measures for the early diagnosis of breast cancer improve the prognosis and survival of patients. This study helps experts research preventive measures against breast cancer through early diagnosis using machine learning techniques. In this project, supervised Learning is used to analyze all features to determine whether a patient is affected by a benign or malignant tumour. The evaluation is performed on several patient datasets that contain features such as radius, texture, perimeter, area, and smoothness. Supervised Learning is a method in which a machine is trained on data in which inputs and outputs are labelled. A model can learn training data and process future data to predict outcomes. Therefore, machine learning techniques are of great importance in the early detection of breast cancer. These techniques support professionals and doctors in the early detection of breast cancer to prevent the development of the disease.