This study explores the prediction and improvement of complex beer flavors using machine learning. By combining extensive chemical and sensory analyses of 250 different beers, the researchers trained machine learning models to predict flavor and consumer appreciation. 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 and unexpected compounds as drivers of beer flavor and appreciation, which can be added to commercial beers to improve their appeal. The study highlights the potential of big data and machine learning in uncovering complex links between food chemistry, flavor, and consumer perception, paving the way for the development of novel, tailored foods with superior flavors. Despite limitations, such as the inability to discern causal relationships and the need for more extensive datasets, the findings provide valuable insights for quality control, product development, and the creation of healthier, tastier beverages.This study explores the prediction and improvement of complex beer flavors using machine learning. By combining extensive chemical and sensory analyses of 250 different beers, the researchers trained machine learning models to predict flavor and consumer appreciation. 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 and unexpected compounds as drivers of beer flavor and appreciation, which can be added to commercial beers to improve their appeal. The study highlights the potential of big data and machine learning in uncovering complex links between food chemistry, flavor, and consumer perception, paving the way for the development of novel, tailored foods with superior flavors. Despite limitations, such as the inability to discern causal relationships and the need for more extensive datasets, the findings provide valuable insights for quality control, product development, and the creation of healthier, tastier beverages.