Wine is a quite popular drink all over the world among different age group of peoples. It is an alcoholic drink made from fermented grapes and many other chemicals are involved in the process of wine making. Several varieties of wine have existed nearly since the earliest days of civilization. In this study, we have applied various supervised classification machine learning algorithms on the dataset of red wine to predict their quality based on the presence of different chemical components labelled in the used dataset. Supervised classification machine learning approaches have received much attention in every industry in the last few years. The majority of machine learning approaches may give remarkably accurate results, compelling most researchers to incorporate them in analytics aimed at prediction. The result shows that despite applying different unique algorithms such as: Decision Tree, Naïve Bayes, Random Forest, Support Vector Machine and Logistic Regression, the accuracy diverges a little. The Random Forests Algorithm generated a highly accurate result followed by Logistic Regression, SVM and Naïve Bayes algorithms. We have encountered the accuracy ranged from 0.71 to 0.77 and the F1 score ranged from 0.69 to 0.78 after applying multiple classifier algorithms.