It has been long-established that wine making is an old craft that requires deep knowledge about the conditions and components that may be present in a wine. The need for quality control has always played a crucial role in the production of wines. Different regulatory agencies stipulate permissible production strategies of using some of the additives and processing agents. Assessing the wine quality using the usual traditional methods is not only tedious but also lack that level of consistency and reproducibility in production. Modern, through the machine learning algorithms it's more fitted to predict with the help of an automatic predictive system infused into a decision support system. In this paper, I have explored different machine learning models for classifying wine quality based on various metrics and components associated to wine quality, the ranking of the wine quality as well as investigation surrounding wine taste differing from another using machine learning models such as Naive Bayes algorithm, K-Nearest Neighbor algorithm, and Support Vector Machines algorithm.