The complexity of predicting wine quality arises from the relationships between physicochemical variables, many of which are intercorrelated and redundant. This paper proposes an integrated wine-quality prediction framework that applies feature compression to obtain a compact representation of the original feature set, and combines this representation with the wine volume information. The compressed feature set is then utilized to train a set of individual classifiers, including Random Forest, Gradient Boosting, XGBoost, and CatBoost. This study combines feature compression with a stacking ensemble approach, which integrates the outputs of individual classifiers and determines the final prediction using Logistic Regression as the meta-classifier. The proposed method consistently achieves superior prediction accuracy and stability compared to individual classifiers by employing feature compression within a stacking framework. Ultimately, this work offers an effective, scalable, and interpretable solution for wine quality evaluation in both practical applications and decision-support systems.