A non-Newtonian stagnation point fluid flow towards two different inclined heated surfaces is mathematically formulated with pertinent effects, namely mixed convection, viscous dissipation, thermal radiations, heat generation, and temperature-dependent thermal conductivity. Mass transfer is additionally considered by the use of a concentration equation. The flow narrating equations are solved numerically by using the shooting method along with the Runge−Kutta scheme. A total of 80 samples are considered for five different inputs, namely the velocities ratio parameter, temperature Grashof number, Casson fluid parameter, solutal Grashof number, and magnetic field parameter. A total of 70% of the data are used for training the network; 15% of the data are used for validation; and 15% of the data are used for testing. The skin friction coefficient (SFC) is the targeted output. Ten neurons are considered in the hidden layer. The artificial networking models are trained by using the Levenberg−Marquardt algorithm. The SFC values are predicted for cylindrical and flat surfaces by using developed artificial neural networking (ANN) models. SFC shows decline values for the velocity ratio parameter, concentration Grashof number, Casson fluid parameter, and solutal Grashof number. In an absolute sense, owning to a prediction by ANN models, we have seen that the SFC values are high in magnitude for the case of an inclined cylindrical surface in comparison with a flat surface. The present results will serve as a helpful source for future studies on the prediction of surface quantities by using artificial intelligence.