ABSTRACT This work highlights the importance of viscoelastic nanofluids in enhancing heat and mass transfer processes due to their elastic nature and complex rheology. The developed predictive model provides deeper insight into their thermal behavior, which is significant for industrial and engineering applications. Here, we utilize neural networks (NNs) based backpropagated Levenberg‐Marquardt (BLM) algorithm to analyze heat and mass transfer in a viscoelastic fluid influenced by electrical and magnetic fields. The study incorporates principles of magnetohydrodynamics (MHD) and the second law of thermodynamics, addressing factors like internal heat generation, Joule dissipation, viscous dissipation, and Darcy dissipation. A reference dataset, generated using the Bvp4c solver, aids in training, validating, and testing the BLM‐NNs across different model scenarios. Key findings highlight that increasing magnetic and viscoelastic parameters reduces fluid velocity, with a greater elastic parameter further slowing it. The absorbent matrix raises temperature, while a higher Prandtl number decreases it. Numerical solutions are compared with analytical models and performance is validated using error histograms, mean square error (MSE), regression analysis, and curve fitting. The results support the reliability and efficiency of the BLM‐NN algorithm. The results of testing, validation, and training for different parameters are presented in tabular form. Numerical results of skin friction, Nusselt number, and Sherwood number under varying physical parameters were also calculated. The outcomes highlight the sensitivity of heat and mass transfer rates to changes in thermal, solutal, and flow‐related parameters.
Akbar et al. (2026) studied this question.