This research examines the boundary-layer flow of a tangent hyperbolic nanofluid over a moving wedge, considering both viscous and radiative effects, in order to evaluate nanoparticle-enhanced thermal properties and non-Newtonian dynamics. The study combines nanofluid heat enhancement with non-Newtonian flow behavior, radiative thermal processes, and motile organism patterns to create an integrated mathematical framework that addresses current research gaps while proposing applications ranging from cooling systems to automotive thermal management, biomedical technology, and energy system design. The differential equations are transformed using similarity transformations before being solved numerically using MATLAB's fourth-order Runge-Kutta technique. The study uses artificial neural networks for prediction validation and findings via contour plots, three-dimensional graphs, and simplified visuals. The study shows that Weissenberg numbers increase fluid elasticity while decreasing drag, heat radiation effects expand temperature profiles while increasing thermal boundary thickness and shifts in thermophoresis, and Lewis numbers have a significant impact on chemical distributions by improving industrial studies of fluid dynamics.

Integration of artificial intelligence into computational fluid dynamics has significantly enhanced the simulation of complex transport phenomena. This review presents a detailed analysis of artificial neural network (ANN) techniques, namely Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG), in the simulation of magnetohydrodynamic (MHD) hybrid nanofluid flows with bio-convection and internal heat generation. Such flows are characterized by strong nonlinearities arising from the synergistic interaction of magnetic forces, nanoparticle-particle interactions, convective effects caused by microorganisms, and heat gradients. Traditional numerical methods, though well-refined, typically face convergence issues, computational costs, and adaptability to highly nonlinear cases. ANN-based models, on the other hand, show remarkable characteristics toward the approximation of intricate physical correlations with enhanced convergence and generalization. This review discusses, in a detailed manner, the theoretical basis, training behavior, prediction accuracy, and computational efficiency of LM, BR, and SCG algorithms in maintaining critical fluid properties like velocity, temperature, and concentration. Special focus is given to the role played by bio-convection in enhancing transport properties and to how ANN techniques effectively model these dynamics with minimal residual error and computational intensity. A comparative study highlights the applicability and uniformity of such neural network algorithms in advanced heat and mass transfer operations with MHD hybrid nanofluids.