Missing substance properties can become a limiting factor in research and process design. Especially for ionic liquids (ILs), experimental data are often not available and measurement is expensive and time-consuming. To cope with this issue, an estimation scheme for the heat capacity and the thermal conductivity of ILs has been developed. To achieve a wide range of application, first-order group contributions have been chosen to describe the molecular structure of the ions. 2419 experimental heat capacity data points for 106 ILs have been used in two separate subsets for parameter fitting and testing to allow for a reliable external validation. In the case of thermal conductivity, 372 data points for 39 ILs have been processed analogously. The data for ILs from the subset not used for the fitting could be reproduced with a mean absolute error of 5.4% in the case of heat capacity and of 8.1% in the case of thermal conductivity. Using these estimation schemes, it is possible to screen a huge number of potential combinations of cations and anions to find candidates suited best for a specific task before performing the experimental measurement.