Milk quality plays a critical role in the dairy industry, impacting not only consumer health but also the overall economic efficiency of dairy production. Ensuring high-quality milk is essential for producing safe dairy products, maintain- ing shelf life, and adhering to regulatory standards. However, determining milk quality based on various parameters such as fat content, pH levels, and microbial content poses significant challenges. Traditional methods of testing milk quality are often time-consuming and prone to human error. In this paper, a machine learning technique has been presented, which, improves the efficiency of classification of milk quality. We utilized several models to analyze a dataset that includes important milk quality parameters. While individual models achieved high accuracies, our hybrid model surpassed them with an impressive 100% accuracy. The confusion matrix and classification report show highly accurate predictions across all categories (high, medium, low). This method not only tackles the difficulties associated with manual testing but also offers a dependable and scalable solution for assessing milk quality in real-time. This benefits both producers and consumers by guaranteeing safer and higher-quality milk.