Quantitative analysis of food components is essential for optimizing food formulation, enhancing the effectiveness of clinical trials, informing nutrition policy, and improving AI applications in the food domain. To support these goals, a suite of advanced analytical tools has been developed for the two most abundant food components: carbohydrates and proteins. These tools significantly improve upon current methodologies by offering high sensitivity and quantitative precision across a broad range of molecular sizes—from monomers to large polymers. For protein analysis, the platform encompasses metabolomic (amino acids and dipeptides), peptidomic (peptides), and proteomic (whole proteins) approaches. Similarly, carbohydrate analysis spans monosaccharides, oligosaccharides, and polysaccharides, corresponding to metabolomic and glycomic levels. Carbohydrates present unique challenges due to their structural complexity and diverse linkage patterns, which often lead to misclassification in nutritional databases and inaccuracies in clinical interpretations. To address these issues, a comprehensive multi-glycomic platform has been developed. Carbohydrates are fractionated into alcohol-soluble and alcohol-insoluble components. The soluble fractions are analyzed using advanced liquid chromatography–mass spectrometry (LC-MS) to identify free mono-, di-, and oligosaccharides. The insoluble fractions undergo three specialized LC-MS workflows that enable detailed monosaccharide quantification, linkage analysis (covering over 100 types), and polysaccharide identification and quantification. These multi-omics methods are rapid, automated, and highly quantitative, allowing for in-depth structural characterization of both whole and processed foods, as well as their interactions with the digestive tract and the gut microbiome. The resulting data not only inform clinical trials and nutrition policy but also enhance AI models by providing detailed structural-functional insights into key food components.