Early disease diagnosis is essential for improving patient outcomes, reducing treatment costs, and enhancing healthcare decision-making. This study presents an integrated framework that combines mathematical modeling with machine learning techniques to support accurate and timely disease prediction. Mathematical models are employed to describe the underlying dynamics and progression of diseases, while machine learning algorithms analyze clinical, laboratory, and medical imaging data to identify complex patterns associated with disease onset. The proposed approach enhances diagnostic accuracy by incorporating both theoretical disease mechanisms and data-driven learning. Performance is evaluated using standard metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating improved predictive capability over conventional diagnostic methods. The integration of mathematical modeling and artificial intelligence provides a robust and interpretable framework for early disease diagnosis, supporting clinicians in making informed decisions. This interdisciplinary approach has significant potential for advancing personalized medicine and improving healthcare outcomes across a wide range of diseases.