Acknowledging the critical importance of precise medical predictions in the realm of healthcare, the advanced machine learning techniques are used to predict precise medical prediction. The primary objective is to conduct a comprehensive analysis of breast cancer datasets, with the goal of enhancing the accuracy of tumour diagnosis. The script strategically employs KMeans clustering as a means of grouping data, enabling a deeper understanding of inherent patterns and structures within the dataset. The focus on data grouping is complemented by the integration of XGBoost and Random Forest models, powerful machine learning algorithms that hold significant promise in predicting breast cancer diagnosis with a high degree of accuracy. Leveraging the strengths of these models, authors aim to provide healthcare professionals with robust tools for optimizing medical data analysis. The synergy between clustering and classification techniques within the script is designed to offer a holistic approach to breast cancer analysis, ensuring that both the exploration of underlying patterns and the accuracy of diagnostic predictions are prioritized.