Abstract

Obesity and type 2 diabetes are heterogeneous, chronic disorders driven by complex genetic, behavioral and 
environmental factors, leading to widely variable responses to lifestyle and pharmacologic therapies. 
Conventional treatment paradigms rely on population averages rather than individual biology, contributing to 
suboptimal control, weight regain and treatment failure. Nanotechnology offers powerful tools to enhance 
bioavailability, tissue targeting and safety of metabolic therapeutics, while artificial intelligence (AI) provides 
data-driven methods for pattern discovery, prediction and optimization across high-dimensional clinical, 
biochemical and behavioral datasets. Integrating AI with nanoformulation design and deployment enables a new 
paradigm: AI-guided nanotherapies tailored to the molecular, phenotypic and lifestyle profile of individual 
patients with obesity and diabetes. This review explores how AI can support rational design of nanoformulations 
(materials selection, composition, size, surface chemistry), predict pharmacokinetics and tissue distribution, and 
match patients to specific nano-enabled interventions. It discusses emerging examples of machine learning in 
nanomedicine and metabolic disease management, the role of digital biomarkers and multi-omics in building 
personalized models, and the architecture of closed-loop systems that couple AI analytics with smart 
nanocarriers and sensors. Key ethical, regulatory and equity considerations are addressed, and future directions 
for AI–nano convergence in “precision diabesity” are outlined.