Early and accurate detection of heart and liver diseases is crucial for improving patient outcomes. This study proposes a machine learning approach for identifying these conditions much sooner, potentially even before symptoms arise. The model will be a powerful tool, trained on a vast dataset of patient information. This data will include clinical signs, laboratory tests, and, if available, imaging scans. By analyzing these parameters, the model will learn to identify patterns associated with various heart and liver diseases.