3rd TAGRA

Neurological disorders represent a global health challenge, contributing to disability, mortality, and reduced quality of life. Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, stroke, and neurodevelopmental disorders involve interactions among genetic, metabolic, inflammatory, vascular, environmental, and lifestyle factors. Increasing attention has therefore focused on nutrition as a modifiable determinant of brain health and neurological outcomes. Smart Nutrition is a data-driven approach integrating nutritional science, artificial intelligence (AI), machine learning, and digital health technologies to develop personalized and adaptive dietary strategies. By combining dietary records with clinical characteristics, metabolic biomarkers, microbiome profiles, and lifestyle data, this approach seeks to identify individual nutritional needs and predict responses to dietary interventions. Particular attention is given to Mediterranean, DASH, MIND, and ketogenic dietary patterns, alongside omega-3 fatty acids, B vitamins, polyphenols, and microbiota-directed dietary fibers. Their potential relationships with neuroinflammation, oxidative stress, mitochondrial function, the gut–brain axis, cognitive resilience, and neurological disease management are discussed. Selected published studies are considered to illustrate how AI and machine learning have been applied to dietary pattern optimization, nutrition-related neurological risk prediction, digital dietary assessment, and prediction of responses to nutritional interventions. These applications may support a transition from generalized dietary advice toward more individualized, evidence-informed strategies. However, observational associations should not be interpreted as causal effects, and promising predictive models do not necessarily demonstrate clinical benefit. Limitations involving data quality, heterogeneous populations, algorithmic bias, interpretability, privacy, and insufficient prospective validation remain substantial. Interdisciplinary collaboration and clinical trials are essential before AI-supported nutritional recommendations can be implemented. Overall, Smart Nutrition provides a promising framework for connecting nutrition, neurological science, and digital technologies while preserving the central role of clinical judgment and robust evidence in patient care. This study will discuss nutrition-based approaches to neurological diseases and evaluates the contribution of Smart Nutrition and AI to personalized neurological care.