Bubble pressures and liquid densities for the propylene (1) + 2-propanol (2) mixture have been determined at (300, 325, and 350) K and x(1) close to 0.20, 0.52, and 0.65 using a vibrating tube densimeter. Liquid densities have been measured from bubble pressures up to 10 MPa and correlated with a multilayer feedforward neural network. The composition dependence of the excess molar volume has been evaluated in the temperature and pressure range of the experimental data. Bubble pressures have been compared with a multilayer feedforward neural network function regressed on the available literature data showing a good consistency between the two. The multilayer feedforward neural network correlations for liquid density and bubble pressure have been used to generate the saturated liquid density surface in the whole temperature range and for compositions including the pure components. Excess molar volumes and bubble pressure predictions obtained by the neural network models have been compared with those from a Peng-Robinson equation of state with Wong-Sandler mixing rules to show the consistent behavior in the regions where data are lacking.