The proposed study delves into investigating blood flow patterns and thermal and solutal transport in an artery affected by stenosis. Specifically, it explores the behavior of blood ingrained with tetra-hybrid nanoparticles and gyrotactic microbes under magnetic effects. To capture the non-Newtonian nature of blood in such complex scenarios, the Maxwell-Oldroyd-B (MO) fluid model is employed. The pertinent nonlinear partial differential equations system accounts for similarity transformations and boundary layer assertions. Numerical results are assessed using a potent shooting approach with a fourth-order Runge–Kutta (RK4) method implemented through MATLAB’s bvp4c commands. The computational outcomes are visualized via graphical representation and tabulated data for in-depth analysis and illustration. The research yields significant insights into various model parameters. For instance, the Lorentz force is observed to exert a drag effect, hindering blood flow velocity, while the opposite trend is observed for blood temperature. Additionally, higher Peclet numbers correlate with reduced mass concentration levels, while gyrotactic microbes’ density profile exhibits an upsurge with elevated Lewis numbers. Notably, the Nusselt number for Newtonian blood is lower than MO blood, shedding light on the impact of blood properties on heat transport. Furthermore, this study proposes a technique based on artificial neural networks (ANNs) for accurately predicting skin friction coefficient, Nusselt number, Sherwood number, and microbes’ density number. The proposed algorithm achieves remarkable accuracy rates of 99.65% on the testing dataset and 99.99% during cross-validation for predicting the skin friction coefficient. The novel findings generated by this model hold promising implications for the treatment and monitoring of arterial diseases, as well as the development of cutting-edge medical devices and technologies.