Evaluation of wine quality has a significant impact on both production methods and consumer preferences in the wine business. This article provides a summary of machine learning-based methods for estimating wine quality that leverage the use of data-driven methodologies to improve the precision and effectiveness of quality assessment. We have considered three types of wines: French Bordeaux wine, red and white variants of the Portuguese “Vinho Verde” wine. There are existing models that incorporate one or two types of wine at a time and perhaps with lower accuracy, which makes it difficult to assess wine quality more objectively and efficiently. Expert opinions on quality are highly individualized and may not reflect consumer preferences. Furthermore, it is possible that the experts will not always be available for the wine tasting. We attempted to design a wine quality prediction approach by applying principles of machine learning, which incorporates easy wine quality assessment and in turn saves time and cost. This project not only emphasizes prediction precision but also opens the possibility of easily incorporating other types of wine. Machine learning algorithms were trained and tested on a holdout original sample. The accuracy ranges around 85% and above which has surpassed the previous works.