Janet Antwi, Obed Akwaa Harrison, Niki Hayatbini
Precision nutrition tailors dietary guidance to individual biological, genetic, metabolic, and behavioral characteristics. Artificial intelligence (AI), machine learning (ML), wearables, sensors, mobile apps, and multi-omics tools increasingly support this approach, but evidence on their effectiveness, feasibility, implementation, and equity remains fragmented. This review evaluated the effectiveness of AI/ML-enabled analytical approaches and technologies in precision nutrition across dietary, metabolic, behavioral, implementation, and equity outcomes. We conducted a PRISMA-guided systematic review and meta-analysis of studies published from January 2015 to December 2025 across six databases. Eligible studies examined AI/ML, digital tools, wearables, mobile platforms, or multi-omics approaches for personalized nutrition across the lifespan. Two reviewers independently screened studies, extracted data, and assessed bias. Random-effects meta-analyses were conducted where outcomes were comparable; remaining studies underwent qualitative and thematic synthesis. Overall, 262 studies met the inclusion criteria. Meta-analyses indicated modest pooled reductions in body weight (-1.97 kg), BMI (-0.57 kg/m2), waist circumference (-2.41 cm), and systolic blood pressure (-4.05 mmHg), whereas no significant pooled effects were observed for lipid outcomes or glycemic markers. Corresponding 95% confidence intervals and 95% prediction intervals were -3.02 to -0.92 and -6.69 to 2.75 kg for body weight, -0.99 to -0.15 and -2.43 to 1.29 kg/m2 for BMI, -4.75 to -0.07 and -11.30 to 6.49 cm for waist circumference, and -6.90 to -1.19 and -13.24 to 5.15 mmHg for systolic blood pressure. Dietary intake outcomes were generally small and inconsistent, although vegetable intake showed a modest improvement. Certainty of evidence was generally low to very low because of risk-of-bias concerns, imprecision, and substantial heterogeneity; prediction intervals for several statistically significant pooled effects crossed the null. Subgroup findings were exploratory and underpowered. Technology-enabled precision nutrition may modestly improve anthropometric outcomes and systolic blood pressure, but longer, well-reported studies are needed to clarify the clinical impact of precision nutrition technologies. SYSTEMATIC REVIEW REGISTRATION: This review was registered with PROSPERO under registration number CRD420251241063.