In the modern era, artificial intelligence (AI) has been applied as one of the transformative factors for scientific research in many fields that could provide new solutions to extremely complicated and complex physical models. In this paper, a multi-layer neural network combined with Bayesian regularization procedure (MNNs-BRP) is utilized to evaluate the model MHD radiative heat in non-uniform heating Eyring Powell fluid (EPF-MHD-RHS). The mixed convection parameter, Prandtl number, and heat emission or immersion parameter are studied in relation to momentum and heat transfer. To facilitate analysis, governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) only with the aid of similarity transformations. From there, a dataset is created and later trained, tested, and validated by the MNNs-BRP model for efficient estimating of fluid models. The MNNs-BRP model is robust and demonstrates high accuracy, which compares well with the benchmark solutions. Performances of which are confirmed by metrics like error histograms, mean squared-error (MSE) check-ups, and regression analysis with MSEs all over the interval [10