Machine learning analysis of 287 older adults (mean age 77.6) revealed that personalized selection of anti-inflammatory interventions—omega-3 fatty acids, losartan, or combination therapy—could theoretically reduce interleukin-6 levels by -0.70 pg/mL annually versus -0.51 pg/mL with standard randomized treatment. The study applied advanced analytics to identify inflammatory and cardiometabolic patterns that predict optimal treatment responses. This represents a significant methodological advance in geriatric medicine, where chronic inflammation drives functional decline and mortality. The precision medicine approach addresses a critical gap: traditional anti-inflammatory medications have consistently failed to improve physical function in older adults, despite reducing inflammatory markers. By matching interventions to individual biological profiles, researchers could potentially overcome this efficacy ceiling. However, the modest effect sizes and non-significant results highlight current limitations. The walking speed improvements were minimal (0.0017 m/s/year), and grip strength showed no benefit over standard care. This likely reflects the complex, multifactorial nature of age-related functional decline that extends beyond inflammation alone. While conceptually promising, the approach requires larger validation studies and more potent interventions to achieve clinically meaningful improvements in physical function.