This study combines extensive chemical and sensory analyses of 250 different beers to train machine learning models that predict flavor and consumer appreciation. For each beer, over 200 chemical properties are measured, and quantitative descriptive sensory analysis is performed with a trained tasting panel. Data from over 180,000 consumer reviews are also used to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, significantly outperforms conventional statistics and accurately predicts complex food features and consumer appreciation from chemical profiles. Model dissection identifies specific compounds as drivers of beer flavor and appreciation, leading to improved commercial beers. The study reveals how big data and machine learning uncover complex links between food chemistry, flavor, and consumer perception, and lays the foundation for developing novel, tailored foods with superior flavors. Predicting and understanding food perception and appreciation is a major challenge in food science. Accurate models could improve quality control, product fingerprinting, counterfeit detection, spoilage detection, and new product development. They also help standardize food assessment methods and could replace trained tasting panels. However, predicting food flavor from chemical properties remains challenging due to the complexity of flavor compounds and the variability in sensory perception. Classical statistics and machine learning methods have been used to predict flavor, but they often ignore complex interactions between compounds. Machine learning models, particularly Gradient Boosting, outperform traditional methods in predicting flavor and appreciation. The study uses extensive chemical and sensory data of 250 commercial beers to develop models that predict taste, smell, mouthfeel, and appreciation from chemical profiles. Beer is well-suited for this due to its complex chemical composition and the availability of large datasets from online reviews. The study identifies key compounds, such as ethyl acetate and ethanol, as drivers of beer appreciation. Public consumer reviews complement expert panel data, providing valuable insights into appreciation scores. Models can predict beer sensory profiles from chemical data, with Gradient Boosting showing the best performance. Model dissection identifies unexpected compounds as drivers of appreciation, highlighting the importance of chemical interactions. Despite limitations, the study demonstrates the potential of machine learning to improve beer flavor and appreciation, offering a stepping stone for future food engineering applications.This study combines extensive chemical and sensory analyses of 250 different beers to train machine learning models that predict flavor and consumer appreciation. For each beer, over 200 chemical properties are measured, and quantitative descriptive sensory analysis is performed with a trained tasting panel. Data from over 180,000 consumer reviews are also used to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, significantly outperforms conventional statistics and accurately predicts complex food features and consumer appreciation from chemical profiles. Model dissection identifies specific compounds as drivers of beer flavor and appreciation, leading to improved commercial beers. The study reveals how big data and machine learning uncover complex links between food chemistry, flavor, and consumer perception, and lays the foundation for developing novel, tailored foods with superior flavors. Predicting and understanding food perception and appreciation is a major challenge in food science. Accurate models could improve quality control, product fingerprinting, counterfeit detection, spoilage detection, and new product development. They also help standardize food assessment methods and could replace trained tasting panels. However, predicting food flavor from chemical properties remains challenging due to the complexity of flavor compounds and the variability in sensory perception. Classical statistics and machine learning methods have been used to predict flavor, but they often ignore complex interactions between compounds. Machine learning models, particularly Gradient Boosting, outperform traditional methods in predicting flavor and appreciation. The study uses extensive chemical and sensory data of 250 commercial beers to develop models that predict taste, smell, mouthfeel, and appreciation from chemical profiles. Beer is well-suited for this due to its complex chemical composition and the availability of large datasets from online reviews. The study identifies key compounds, such as ethyl acetate and ethanol, as drivers of beer appreciation. Public consumer reviews complement expert panel data, providing valuable insights into appreciation scores. Models can predict beer sensory profiles from chemical data, with Gradient Boosting showing the best performance. Model dissection identifies unexpected compounds as drivers of appreciation, highlighting the importance of chemical interactions. Despite limitations, the study demonstrates the potential of machine learning to improve beer flavor and appreciation, offering a stepping stone for future food engineering applications.