Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection and accurate diagnosis are critical for effective treatment and prevention. Data mining techniques have emerged as powerful tools for analyzing large datasets to extract meaningful patterns and make predictions. This research paper aims to explore the application of data mining in predictive diagnosis for cardiovascular diseases. The study will start by collecting a comprehensive dataset comprising patient information, including demographics, medical history, lifestyle factors, and diagnostic test results. Various data mining techniques, such as classification, clustering, and association rule mining, will be applied to uncover hidden patterns and relationships within the data. Feature selection methods will be employed to identify the most relevant attributes for accurate prediction. The research will investigate different predictive models, including decision trees, support vector machines, and neural networks, to develop a reliable diagnostic system. Model performance will be evaluated using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, the study will employ cross-validation techniques to ensure the generalizability and robustness of the developed models. The research will explore the integration of advanced techniques, such as deep learning and ensemble methods, to enhance the predictive accuracy of the diagnosis. The use of explainable AI techniques will also be considered to provide interpretable insights into the predictive models' decision-making process. The findings of this research will contribute to the advancement of predictive diagnosis for cardiovascular diseases by leveraging data mining techniques. The developed diagnostic models will assist healthcare professionals in making accurate and timely predictions, leading to improved patient outcomes, personalized treatment plans, and effective preventive measures.