Data-Driven Nanoemulsion Design: Integrating Artificial Intelligence With Formulation Science Publisher



Ketabchi N
Source: Journal of Research and Innovation in Food Science and Technology Published:2026

Abstract

Nanoemulsions are versatile colloidal delivery systems increasingly used in foodpharmaceutical, cosmetic, nutraceutical, and agricultural applications owing to their ability to improve the solubilization, stability, and bioavailability of poorly water-soluble compounds. However, their development remains a complex multivariable process in which formulation composition and processing conditions interact nonlinearly to determine criticaquality attributes, making conventional trial-and-error approaches and classical design oexperiments increasingly inefficient. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for predictive formulation design, experimentaprioritization, and multi-objective optimization. Although recent reviews have discussed AI-assisted formulation development, a comprehensive critical evaluation focused specifically on AI-driven nanoemulsion formulation, its unique physicochemical challenges, and its translation to food and pharmaceutical applications remains limited. This review critically evaluates current AI and ML approaches for nanoemulsion development by integrating physicochemical principles with recent advances in predictive modeling. It compares major AI methodologies, summarizes nanoemulsion-specific applications, and discusses currenchallenges related to data availability, model reliability, and practical implementation. Overall, AI is expected to serve as a decision-support framework that complements physicochemical understanding and experimental expertise rather than replacing themFuture progress will depend on standardized datasets, rigorous model validation, and closer integration of AI with experimental formulation strategies to accelerate the development orobust and scalable nanoemulsion systems for food and pharmaceutical applications. © 2026, Research Institute of Food Science and Technology. All rights reserved.