Breast cancer is a widespread and possible life-threatening disease, making early detection critical for successful treatment. This research aims to develop an innovative approach for breast cancer detection by grasping machine learning algorithms to predict the disease. The project begins by collecting a diverse dataset comprising a wide range of breast cancer tumour metrics such as, mean, Standard Error(se) and worst. Subsequently, a machine learning model is trained on this dataset to identify patterns and relationships between reported tumour metrics and diagnosed cases of breast cancer. The developed model incorporates advanced algorithms to analyse the severity of the breast cancer. Additionally, the system aims to provide personalized risk assessments, taking into account demographic factors and medical history. To enhance the model's accuracy, feature selection techniques are employed to identify the most relevant symptoms contributing to the predictive capability. The project also emphasizes the importance of interpretability in the machine learning model, allowing healthcare professionals to understand and trust the system's output. To facilitate seamless integration into clinical practice, a user-friendly interface is developed, enabling easy input of patient symptoms and interpretation of the model's results. The proposed system holds the potential to serve as a valuable tool for healthcare providers in identifying individuals at risk of breast cancer based on reported tumour metrics. By promoting early detection and timely medical attention, the project aims to contribute to the overall improvement of breast cancer detection rates.