The assessment of wine quality is of paramount importance to both consumers and the wine industry. Recognizing its impact on customer satisfaction and business success, companies are increasingly turning to product quality certification to enhance sales in the global beverage market. Traditionally, quality testing was conducted towards the end of the manufacturing process, resulting in time-consuming and resource-intensive procedures. This approach involved the engagement of multiple human experts to evaluate wine quality, leading to high costs. Moreover, since taste perception is subjective and varies among individuals, relying solely on human specialists for assessing wine quality presents significant challenges. Our research focuses on advancing the quality of wine prediction by leveraging diverse characteristics of wine. We applied various feature selection techniques and explored machine learning algorithms to identify the optimal combination of parameters for accurate wine quality prediction. This approach reduces the time and costs associated with traditional quality assessment methods and provides a more standardized and consistent evaluation process. Our findings contribute to the advancement of wine industry practices, enabling businesses to make informed decisions and deliver high-quality products that meet consumer expectations.