Many scientific and technological disciplines in the specialization of chemical and petroleum sectors are related to multilayer fluid flow systems. In light of this perspective, the current article focuses on the steady, laminar flow of mono nanofluid saturated with porous media sandwiched between a viscous fluid filled in a vertical channel to develop a new model. The main subject of this scientifical research is to explore the prediction analysis through machine learning algorithm for the physical quantities across the entire region of the proposed fluid. This present physical phenomenon is witnessed in the mathematical form of nonlinear PDEs through the conservation principles and Darcy’s law. The system of governing PDEs are nondimensionalized by treating the suitable fundamental variables to facilitate the simulation process. After that, the dimension-free form of momentum and temperature equations is numerically simulated by utilizing the Wolfram language algorithm. In addition, the comparison analysis is accounted to find the higher heat transfer performance for the particles After ensuring the numerical computation for the specific flow scenarios, an innovative machine learning-based multiple linear regression technique is employed to anticipate the flow and heat transfer rate for the inclusion of Ag nanoparticle. The validation of the present study is highlighted by comparing with the existing literature. The current findings exhibit that the region of single-phase nanosized particles suspended in water affects the momentum and thermal behavior of the clear viscous fluid regions. Silver Ag nanosized particles exhibit better performance in heat transfer than the MgO and Also, the MLR approach offers a new viewpoint on flow control with higher accuracy 99.59% and lower error The outcomes of the suggested techniques enrich the application of the multilayer fluid flow model, specifically fire suppression systems, petroleum sectors, agricultural sprays, and oil recovery.