Develop the most optimal model to facilitate rapid machine learning for the swift and accurate prediction of whether a tumour is malignant or benign. In the event of a malignant diagnosis, the patient will be recommended to seek immediate medical attention at a nearby hospital. The aim of this research is to construct a model that streamlines the process of recognizing patterns and structure within input data. This model will be applied to two distinct breast cancer datasets with the goal of swiftly and accurately predicting malignancy or benign status while minimizing errors. The most accurate clustering method for these two breast cancer datasets will be determined by evaluating various techniques, including K-mean clustering, Mean-Shift clustering, Spectral clustering, Gaussian Mixture with Expectation Maximization (EM) clustering, and Gaussian Mixture with Variational Inference (VI) clustering. In this research, the same algorithms are used on two datasets. Gaussian Mixture with Expectation Maximization (EM) Cluster, Spectral Cluster, and K-means Cluster demonstrated the highest accuracy, the shortest processing time, and the lowest error rates in both breast cancer datasets. Specifically, K-mean clustering, Spectral Clustering, and Gaussian Mixture with Variational Inference (VI) clustering prove to be particularly valuable in the healthcare sector for real-time breast cancer data analysis, as they excel in swiftly identifying input patterns and structures.