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Simple Summary: Obesity is a significant health concern associated with fat accumulation and complications like inflammation, cancer, and diabetes. Understanding its molecular roots is crucial, especially for young individuals who are capable of lifestyle changes. Our study examined underweight, lean, overweight, and obese individuals by analyzing blood samples using metabolomics, proteomics, and genomics. Using metabolomics, we identified 313 substances; proteomics revealed 708 proteins, and genomics explored 647,250 point mutations. Models predicting body mass index showed that, individually, proteomics provided more value for prediction, followed by metabolomics and genomics. Combining proteomics and metabolomics in a multiomic approach yielded the best results, surpassing single-factor analyses. This pioneering study is the first to focus on obesity in young people, integrating genomic, proteomic, and metabolomic data. Our findings provide valuable insights into the molecular mechanisms of obesity, offering potential for more targeted interventions. Obesity is a socially significant disease that is characterized by a disproportionate accumulation of fat. It is also associated with chronic inflammation, cancer, diabetes, and other comorbidities. Investigating biomarkers and pathological processes linked to obesity is especially vital for young individuals, given their increased potential for lifestyle modifications. By comparing the genetic, proteomic, and metabolomic profiles of individuals categorized as underweight, normal, overweight, and obese, we aimed to determine which omics layer most accurately reflects the phenotypic changes in an organism that result from obesity. We profiled blood plasma samples by employing three omics methodologies. The untargeted GC×GC–MS metabolomics approach identified 313 metabolites. To augment the metabolomic dataset, we integrated a label-free HPLC–MS/MS proteomics method, leading to the identification of 708 proteins. The genomic layer encompassed the genotyping of 647,250 SNPs. Utilizing omics data, we trained sparse Partial Least Squares models to predict body mass index. Molecular features exhibiting frequently non-zero coefficients were selected as potential biomarkers, and we further explored enriched biological pathways. Proteomics was the most effective in single-omics analyses, with a median absolute error (MAE) of 5.44 ± 0.31 kg/m2, incorporating an average of 24 proteins per model. Metabolomics showed slightly lower performance (MAE = 6.06 ± 0.33 kg/m2), followed by genomics (MAE = 6.20 ± 0.34 kg/m2). As expected, multiomic models demonstrated better accuracy, particularly the combination of proteomics and metabolomics (MAE = 4.77 ± 0.33 kg/m2), while including genomics data did not enhance the results. This manuscript is the first multiomics study of obesity in a gender-balanced cohort of young adults profiled by genomic, proteomic, and metabolomic methods. The comprehensive approach provides novel insights into the molecular mechanisms of obesity, opening avenues for more targeted interventions. [ABSTRACT FROM AUTHOR]