In this study, lid-driven (LD) cavity flow with a slip condition through the moving lid is numerically and statistically studied. Firstly, the two dimensional, time dependent dimensionless governing equations are numerically resolved using the Radial Basis Function (RBF) method in space and the second order backward differentiation formula (BDF2) in time. The numerical results reveal that the resistance at the top boundary is reduced by the presence of slip parameter, and the flow features change inside the cavity. Secondly, datasets from the numerical results are collected involving Reynolds number and slip parameter as inputs, and some velocity indicators as targets. Then, Neural Network (NN) modeling for these indicators is processed. In terms of mean squared error metric, it is found that the predicted results on test data are very well suited. Instead of repeated numerical computations, NN modeling allows one to instantly determine the thermal and fluid behavior of the system at the required problem parameters.