This study examines the temporal stability of patient biochemical phenotypes through unsupervised clustering of serum biomarkers over a four-year period (2020–2024). Using robust statistical methods on a large cohort (n = 20,990), we identified ten distinct biochemical clusters characterized by specific patterns in inflammation markers, renal function, and metabolic indicators. Analysis revealed significant associations between cluster membership and demographic factors (age, sex), with high classification stability (cross-validation accuracy: 0.901 ± 0.002). Longitudinal analysis demonstrated remarkable temporal consistency in cluster prevalence, with eight clusters showing less than 1% change in prevalence between 2020 and 2024. Our findings suggest the existence of stable biochemical phenotypes that persist over time despite variations in patient populations, potentially representing fundamental pathophysiological states. This work provides a foundation for precision medicine approaches in patient stratification and suggests that biochemical phenotyping through unsupervised learning may identify clinically relevant patient subgroups with consistent presentations over time.