AbstractNatural deep eutectic solvents (NADES) are mixtures of naturally derived compounds with a significantly decreased melting point owing to specific interactions among the constituents. NADES have benign properties (low volatility, flammability, toxicity, cost) and tailorable physicochemical properties (by altering the type and molar ratio of constituents); hence, they are often considered to be a green alternative to common organic solvents. Modeling the relation between their composition and properties is crucial though, both for understanding and predicting their behavior. Several efforts have been made to this end. This Review aims at structuring the present knowledge as an outline for future research. First, the key properties of NADES are reviewed and related to their structure on the basis of the available experimental data. Second, available modeling methods applicable to NADES are reviewed. At the molecular level, DFT and molecular dynamics allow density differences and vibrational spectra to be interpreted, and interaction energies to be computed. Additionally, properties at the level of the bulk medium can be explained and predicted by semi‐empirical methods based on ab initio methods (COSMO‐RS) and equation of state models (PC‐SAFT). Finally, methods based on large datasets are discussed: models based on group‐contribution methods and machine learning. A combination of bulk‐medium and dataset modeling allows qualitative prediction and interpretation of phase equilibria properties on the one hand, and quantitative prediction of melting point, density, viscosity, surface tension, and refractive index on the other. Multiscale modeling, combining molecular and macroscale methods, is expected to strongly enhance the predictability of NADES properties and their interaction with solutes, and thus yield truly tailorable solvents to accommodate (bio)chemical reactions.