Neurocomputing Modeling of Hydromagnetic Nanofluid Flow With Chemical Reactions on Fluctuating Sheets

Industrial applications involving coupled mass and heat transfer have motivated the present study of steady, two‐dimensional magnetohydrodynamic (MHD) nanofluid flow on a linearly stretching sheet, with viscous dissipation, temperature‐dependent viscosity, chemical reactions, and Soret effects. Traditional numerical solvers remain computationally intensive for extensive parametric analyses of these highly nonlinear multiphysics systems, particularly when fully coupled impacts are considered over stretching surfaces. This study addresses the gap by developing a novel hybrid machine learning framework, a feed‐forward artificial neural network (FFANN) with 10 hidden neurons and log‐sigmoid activation, optimized through the Levenberg–Marquardt backpropagation algorithm (FFANN‐BLMA), trained on high‐fidelity reference data. The governing partial differential equations are reduced to nonlinear ordinary differential equations via similarity transformation and solved accurately using the fourth‐order Runge–Kutta method in Mathematica. The trained model demonstrates exceptional predictive performance, with absolute errors ranging from 10 −6 to 10 −9 across the dimensionless velocity f ′ , temperature g , and concentration h profiles for nanoparticle volume fractions ϑ = 0.1, 0.2, and 0.3. Mean squared errors reach the order of 10 −12 to 10 −14 , and regression indices approach unity ( R ≈ 1), confirming the robustness and accuracy of the FFANN‐BLMA framework. Parametric experiments indicate that the amplification of ϑ suppresses the velocity profile while thickening the thermal and solutal boundary layers due to augmented effective thermal conductivity in addition to species diffusivity. The suggested FFANN‐BLMA paradigm offers a computationally efficient, precise, and scalable alternative to the traditional numerical solvers that allows rapid design optimization and exploration of parameters in industrial thermal systems, such as electronics cooling, stretch‐coating operations, and energy‐efficient heat exchangers. This article highlights the transformative nature of machine learning in the development of modeling capabilities in applications of complex fluid dynamics and heat transfer.