Early disease detection is crucial for effective treatment and improved patient outcomes. Traditional methods of disease detection rely on manual analysis of medical data, which can be time-consuming and prone to errors. The advent of Artificial Intelligence (AI) has transformed the field of disease detection, enabling rapid and accurate diagnosis. This review paper compares traditional and AI-driven methods of disease detection, focusing on Parkinson’s, cancer, metabolic, and genetic diseases. We discuss the advantages and limitations of AI-based approaches, including machine learning and deep learning, and their potential to revolutionize disease detection.