Milk is the basic need of people in their day-to-day life. The quality of milk is an important factor to take into consideration for optimal health because it is consumed by all age groups in various ways. In this study, we used multiple machine learning models to estimate quality of milk based on 7 different parameters. pH, temperature, taste, odor, fat, turbidity, and color are the parameters taken into consideration. To predict the quality of milk, various M.L models used were Decision Tree Classifier, Gradient Boosting, Multi-layer Perceptron, Random Forest Classification, Multinomial Regression, KNN, Support vector classifier, XGB, Naïve Bayes. Our analysis found that odor and turbidity were the most significant features for predicting milk quality. It was observed that, By comparing all the models based on evaluation metrics and confusion matrix it was found that gradient boost model was the best among all the other models. The developed models can be useful for predicting milk quality in real-time and improving the efficiency of milk quality control in the dairy industry.