Abstract: With therapid advancement of medical science and technology, machine learning has become an integral component of precision medicine. However, traditional machine learning methods often focus on the correlation analysis between independent and outcome variables, which can hinder their ability to accurately identify causal relationships. This limitation leads to biased clinical conclusions, impacting the precision and reliability of clinical decision-making. Causal machine learning has emerged as a promising approach to overcome these limitations, and its application in the medical field is expanding. This paper systematically explores the definition and core concepts of causal machine learning, clarifies its specific application scope in disease prevention, and delves into its unique advantages over traditional machine learning, statistical models, and causal inference. Furthermore, it outlines the practical methodologies, operational steps, and key technical aspects of causal machine learning in medical practice. By presenting specific case studies, the paper discusses the applications of causal machine learning in disease prevention, highlighting both its strengths and weaknesses. Ultimately, the goal is to standardize and promote the scientific application of causal machine learning in the medical domain.