Milk adulteration is a widespread problem globally, affecting public health and the dairy industry. Traditional methods of detecting adulteration are labor-intensive, time-consuming, and require sophisticated instruments. This paper reviews the potential of machine learning (ML) techniques to detect milk adulteration effectively. By analyzing chemical and physical properties of milk using advanced ML models, this study highlights the advancements in automation and optimization in detecting adulterated milk. The review includes an evaluation of various machine learning algorithms applied to the detection of adulterants in milk, offering insights into their accuracy, efficiency, and applicability in real-world scenarios