In the present research, artificial intelligence based backpropagated neural networks with Levenberg-Marquardt algorithm (BNN-LMA) are utilized to interpret the numerical computation for squeezing 2D magneto-hydrodynamic (MHD) nanofluid flow between two parallel plates. The non-linear system of ODEs represents the magneto-hydrodynamic, squeezing nanofluidic flow model (MHD-SNFM). A reference dataset for BNN-LMA is formulated by utilizing Adam numerical solver for different scenarios of MHD-SNFM by variation of squeezing number, Hartmann Number and heat source parameter. The validation, testing and training processes of BNN-LMA are exploited to analyze the approximate solution of MHD-SNFM for different scenarios and correctness of proposed BNN-LMA is verified by comparison of reference outcomes. The performance of BNN-LMA to solve the MHD-SNFM is validated through regression analysis, histogram studies and mean square error (MSE).