This study aims to evaluate the performance of machine learning models in breast cancer diagnosis. The dataset includes a dataset of clinical characteristics related to breast cancer diagnosis. The models used in the study include Random Forest, Decision Tree, Naive Bayes, Gradient Boosting, AdaBoost and Bagging. First of all, the data set was converted to a suitable format for machine learning models and divided into trainingtest data sets. Then, different models were trained and evaluated on the test dataset. Accuracy values   and confusion matrices were calculated and visualized for each model. Performance metrics include accuracy, precision, recall, F1 score, and ROC AUC. These metrics were used to evaluate the classification abilities of the models. The ROC curve of each model was also plotted and the ROC AUC values   were reported. The results show that different models can be used successfully in breast cancer diagnosis. However, it has been observed that models such as Gradient Boosting and AdaBoost exhibit higher performance than others. These findings suggest that machine learning models can be used effectively for breast cancer diagnosis and may be helpful in clinical applications.