This study addresses the critical need for more accessible cardiac diagnostics and personalized patient care in the face of a global cardiovascular disease epidemic. A novel method for generating a complete 12-lead electrocardiogram (ECG) from a single Lead I ECG is presented, while utilizing a modified version of an advanced deep learning framework known as the Attention U-Net. This technology improves access to comprehensive cardiac assessments, allowing for the diagnosis of a wide range of abnormalities such as ischemic heart disease, cardiac conduction disorders, and myocardial infarctions. Furthermore, the researchers expand this research and intend to provide personalized dietary programs based on individual patient profiles, including age, gender, Body Mass Index (BMI), diabetes, cholesterol levels, and heart disease status. This comprehensive approach integrates ECG reconstruction through which diagnosis of cardiac diseases could be provided conveniently. This approach tailors dietary planning as well, paving the door for more efficient treatment of cardiovascular health.