Breast cancer is the biggest cause of death among women worldwide. Early identification and diagnosis can significantly enhance breast cancer survival and treatment.Breast cancer mortality are increasing substantially each year. It is the most frequent type of cancer and the leading cause of death among women worldwide. Any progress in cancer detection and prognosis is critical for living a long and healthy life. As a result, having a high level of accuracy in cancer prognosis is crucial for updating patient survival standards and treatment options. As technology evolved, breast cancer prediction models were created utilising machine learning and artificial intelligence. The study attempts to forecast breast cancer using data visualisation and machine learning techniques. Data Visualisation Techniquesare used to investigate and analyse datasets, discovering critical factors that influence prediction performance. We utilise a permutation-based approach to calculate feature relevance and visually represent it with bar graphs.This study demonstrates that machine learning algorithms and data visualisation can accurately forecast breast cancer.Machine learning approaches have proven to be a potent technology, have become a research hotspot, and can make a substantial contribution to the early diagnosis and prediction of breast cancer. In this study, we used the Breast Cancer dataset from Kaggle to test five machine learning algorithms: Support Vector Machine (SVM), Discriminat, Logistic Regression, Naive Baye's (C4.5), and K-Nearest Neighbours.After receiving the results, the different classifiers' performance is reviewed and compared. The fundamental purpose of this research work is to discover the most efficient machine-learning algorithms for breast cancer diagnosis and prediction based on confusion matrix, accuracy, and precision. The Support Vector Machine is demonstrated to have achieved the The greatest accuracy is 97.6%, beating all other classifiers.This shows a lower level of performance than others. The outcomes can provide substantial information to healthcare practitioners, helping to improve diagnostic and therapeutic options for breast cancer patients.