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Abstract

In this study, a numerical solution is developed for the steady flow of a power-law fluid influenced by magnetohydrodynamics (MHD) effects over a continuously moving, linearly stretching surface. The model incorporates mass transfer involving species concentration and a one-stage chemical reaction under isothermal and homogeneous conditions. Reactive species emitted from the surface diffuse into the fluid, driven solely by the sheet's motion.The model examines the effects of six key parameters: power-law index, magnetic parameter, modified Schmidt number, reaction rate parameter, wall concentration parameter, and local Grashof number. These factors significantly influence the mass transfer characteristics and fluid flow behavior.To solve the governing equations, an artificial neural network (ANN) combined with the Levenberg-Marquardt Algorithm (LMA) is employed. The differential equation is reformulated into an ANN-based optimization problem, minimizing the mean square error as a fitness function. The ANN-LMA results are compared graphically and numerically with reference solutions for validation.Graphical results show that the magnetic field increases surface skin friction but slightly decreases the mass transfer rate. However, the mass transfer is highly sensitive to changes in the modified Schmidt number and the reaction rate parameter, increasing with their higher values. Statistical measures such as mean, standard deviation, minimum, and maximum values are used to further validate the solution.Overall, the study highlights the importance of exploring non-Newtonian fluid models, especially those exhibiting shear-thinning behavior, which appears to lower wall shear stress and adds complexity to power-law fluid dynamics.