<p>This research is a healthy addition in terms of a novel design application to the list of stochastic numerical techniques where magnetohydrodynamic Williamson nanofluid boundary layer flow is scrutinized through an exponentially stretching sheet with heat transfer characteristics using the Poisson wavelet neural networks. The design of proposed scheme is composed of artificial neural networks, and a hybrid process is adopted in which the numerical outcomes obtained using a global search solver named the genetic algorithms are refined through a local search technique named sequential quadratic programming. The phenomenon of similarity transformations is adapted to convert the governing equations of the suggested fluid model into a dimensionless framework of nonlinear ordinary differential equations, which is investigated numerically through the newly designed methodology for various scenarios constructed on behalf of distinct physical parameters that pre-exist in the problem to scrutinize the velocity and thermal profile. Thermal analysis reveals that an increase in Williamson parameter leads an increase of 15% in the thermal profile because of shear thinning effect. It is also concluded that a higher porosity level contributes to a 20% increase in nanofluid temperature as nanoparticle collisions strengthen the heat transfer process. Graphical as well as tabulated form visualization of absolute errors and statistical operator-based performance analyses of the proposed methodology are also integral to this research.</p>