Breast cancer is one of the leading causes of death among women worldwide, and early detection is essential for improving survival rates. This project presents an Explainable Artificial Intelligence (XAI)-based Breast Cancer Prediction and Decision Support System using the Breast Cancer Wisconsin dataset, where machine learning algorithms such as Logistic Regression, Decision Trees, and Random Forest are applied to classify tumors as benign or malignant based on features extracted from fine needle aspirate images. To overcome the limitations of traditional black-box models, the system integrates explainability techniques like SHAP and LIME, which provide clear insights into how different features influence the model’s predictions, thereby enhancing transparency and trust. The model is trained and tested on the dataset to achieve high accuracy while also offering interpretable results, enabling healthcare professionals to understand the reasoning behind each prediction. Overall, the proposed system not only improves diagnostic accuracy but also supports clinical decision-making by combining reliable predictions with meaningful explanations, making it a valuable tool for early breast cancer detection and treatment planning