The pervasive and economically impactful disease in dairy animals, mastitis, presents a significant challenge to the global dairy industry. Accurate and prompt diagnosis is paramount for effective treatment and to curb the spread within the herd. Leveraging advancements in machine learning for disease identification, this article reviews the application of ensemble models in detecting mastitis in cows. The document outlines mastitis, covering its origins, symptoms, and implications for milk supply and animal well-being, emphasizing the limitations of traditional diagnostic methods. It underscores the need for automated, reliable detection strategies and offers a thorough assessment of ensemble machine learning models consists of random forest, gradient boosting, bagging, and stacking. The evaluation scrutinizes model performance, considering datasets, feature selection methods, model designs, and assessment metrics. This review serves as a valuable resource for researchers, veterinarians, and dairy industry professionals seeking to implement machine learning algorithms for mastitis identification. Providing insights into the current landscape pertaining to cow mastitis worldwide , identifying knowledge gaps, and proposing solutions for enhanced accuracy, the review contributes to advancing mastitis detection, ultimately improving cow health and productivity.