This work develops an inverse methodology to jointly estimate the thermal and mass diffusivities of a solute-solvent system. An inverse parameter estimation framework using a combination of evolutionary simulation and optimization algorithms namely Artificial Neural Network (ANN) and Genetic Algorithm (GA) is discussed. The direct problem is first solved by assuming thermal and mass diffusivities as constants and then as functions of temperature and concentration. Synthetic experimental data is generated by sprinkling noise sampled from three probability distributions namely Gaussian, Uniform and Logistic on the data obtained from direct numerical simulations carried out using prescribed values of diffusivities. This data is then fed into the inverse framework to estimate the parameters of interest and systematically quantify the associated uncertainties. In case of diffusivities dependent on temperature and concentration both, the higher order parameters are also jointly retrieved and associated uncertainties are quantified. Three sets of experiments namely diffusion of heat in water due to applied temperature difference, isothermal diffusion of salt solution in fresh water and diffusion of salt solution in fresh water along with simultaneous counter current heat diffusion are performed. The time evolving temperature and concentration fields are captured in the form of an interferogram using a Mach-Zehnder interferometer illuminated by a monochromatic laser source (Helium-Neon laser, λ = 632.8 nm). The temperature and concentration data thus obtained is used to estimate the thermal and mass diffusivities of the solute-solvent system using the inverse framework. The results obtained are compared against literature to validate the proposed parameter estimation methodology. © 2017 Elsevier Masson SAS