Normative databases in ophthalmology provide essential reference values derived from healthy individuals, enabling clinicians to detect early signs of ocular disease by comparing patient data to population-based benchmarks. These databases are particularly critical in optical coherence tomography (OCT), supporting the assessment of retinal, choroidal, and optic nerve structures. Stratified by age, gender, and ethnicity, normative datasets account for biological variability and enhance both clinical interpretation and research validity. Sample size, demographic diversity, and device-specific differences influence the reliability of these references. As imaging technologies and artificial intelligence evolve, normative databases will increasingly drive precision diagnostics, individualized care, and advancements in vision science research.