Obesity is a complex, multifactorial chronic disease characterized by excessive and/or aberrant adiposity that requires interventions beyond conventional calorie-restriction models. Current clinical guidelines converge on behavioral modification, encompassing nutritional therapy, physical activity, stress reduction, and sleep improvement, as the cornerstone of obesity management, with psychological therapy, pharmacotherapy, and bariatric procedures as adjunctive or escalating options. While traditional "one-size-fits-all" dietary approaches frequently yield suboptimal long-term outcomes, precision nutrition has emerged as a promising strategy for individualized obesity care. This review critically appraises the evidence underlying each pillar of precision nutrition: genetic profiling shows clear utility in monogenic obesity but variable benefit when diet is matched to common polygenic variants; microbiome-targeted studies reveal that the Bacillota to Bacteroidota ratio shows inconsistent associations with obesity across studies, with genus-level and functional alterations offering more reproducible targets; and metabolomic profiling of postprandial glycemic responses has progressed from foundational predictive algorithms to large-scale clinical validation. It also highlights chrono-nutrition, the alignment of food timing with individual chronotype, as an emerging pillar of personalization. Beyond dietary composition, the review addresses how personalized nutrition can mitigate the adverse effects of incretin-based pharmacotherapy and discusses how artificial intelligence can integrate multi-omics and behavioral data into real-time dietary guidance. Future clinical implementation will require overcoming challenges related to data integration, predictive accuracy, and accessibility, paving the way for more effective, evidence-based obesity management.
