The primary goal of this research study is to examine the influence of Brownian motion and thermophoresis diffusion with the impact of thermal radiation and the bioconvection of microorganisms in a symmetrically stretching sheet of non-Newtonian typical Williamson fluid. Structures of the momentum, energy, concentration, and bio-convection equations are interconnected with the imperative partial differential equations (PDEs). Similarity transformations are implemented to translate pertinent complicated partial differential equations into ordinary differential equations (ODEs). The BVP4C approach from the MATLAB assemblage computational methods scheme is extensively impacted by the results of these ODEs. The impact of several physical parameters, including Williamson fluid $We(0.2\le We\le 1.2)$, the magnetic field parameter $M(0.0\le M\le 2.5)$, Brownian motion $Nb(0.0\le Nb\le 1.0)$, thermophoresis diffusion $Nt(0.1\le Nt\le 0.9)$. In addition, various physical quantities of the skin friction ($\sqrt{R{e}_x}{C}_{f_x}$), Nusselt number ($N_{u_x}$), Sherwood number ($S_{h_x}$), and motile microorganisms ($N_{n_x}$) are occupied and demonstrate the visualization of graphs and tabular values. These outcomes are validated with earlier obtained results, displaying excellent synchronicity in the physical parameters. Furthermore, the physical quantities concerning the non-dimensional parameters are anticipated by employing Multiple Linear Regression (MLR) in Machine Learning (ML) as successfully executed a novelty of this study. These innovative techniques can help to advance development and technologies for future researchers. The real-world implications of this research are that bio-remediation, microbial movements in mixed fluids, and cancer prevention therapy are crucial.

The main objective of this research is to examine the radiation effects of an unsteady MHD Williamson biofluid (Blood) over a wedge that interacts with thermophoresis diffusion and Brownian motion. The necessary prerequisites of partial differential equations (PDEs) ensure the development of suitable mathematical frameworks for momentum, energy, and concentration. These appropriate nonlinear PDEs are frequently transmuted into ordinary differential equations (ODEs) by implemented similarity transformation. The results of these ODEs have a significant impact on the BVP4C approach from the MATLAB package computational structures. The graphs and tabular data provided the various values for pertinent parameters on the non-dimensional velocity temperature, concentration profiles, and the numerical values of skin friction, Nusselt number, and Sherwood number were found and discussed in detail. A novel aspect of the research effort was the effective incorporation of multiple linear regression (MLR) employing machine learning (ML), a statistical technique to forecast the physical quantities for present numerical results with an accuracy of 95%. Finally, the response and predicted variables were verified using linear regression. The potential benefit of these outcomes is to develop novel therapeutic and diagnostic strategies for cancer treatment, as well as for a better understanding of medical problems and designing more effective drug delivery systems. In particular, for significant developments in computer technology and resources, the enormous computational cost of these simulations still keeps them from becoming a clinical tool. An additional benefit is that the outcomes showed acceptable congruence with the tangible findings of recent research and enlargements for future investigators.