Historical menus serve as valuable archival records that reflect culinary practices, consumption patterns, and environmental changes. However, limited research has explored their potential in understanding shifts in culinary trends. This study addresses this gap by investigating the effectiveness of word embeddings and optimization techniques in classifying cuisine patterns using historical menu data. By comparing traditional, ensemble, and deep learning models, the paper focuses on using word embeddings for multi-class classification. TF-IDF embeddings consistently provided superior performance, especially in traditional models like Support Vector Machines and ensemble methods such as Random Forest, achieving the highest accuracy, 0.9059, and F \(_1\) -score, 0.9011, when combined with Word2Vec. Findings indicate that selection of mere embedding does not effect the model’s performance for this classification task, hybrid embeddings with hyperparameter optimization improve classification performance. Future research should investigate transformer-based models to better capture ingredient and cuisine nuances.