<p>This study introduces a dynamic electromagnetic channel to precisely control milk flow and its thermal properties, effectively addressing the non-linear thermal response of milk. By integrating artificial intelligence (AI), a newest analytical framework is crafted, enabling detailed modeling of complex interactions among milk flow, hybrid nanoparticles, and dynamically varying thermal environments. This enhanced modeling is crucial for accurately predicting and optimizing key dairy processes like pasteurization and homogenization. The research focuses on the thermal and flow dynamics of milk infused with silver and zinc oxide nanoparticles (Ag-ZnO/milk) in a rapidly moving electromagnetic channel with oscillating ramped thermal boundary conditions. The model integrates essential physical phenomena such as radiant heat emission and Darcy drag forces, employing Darcy’s model to shed light on the drag within the porous medium. The flow of milk is thoroughly analyzed through mathematical and physical perspectives, with equations efficiently solved using the Laplace Transform (LT) computational scheme. Analytical results are examined in depth, with key parameters like flow profiles, shear stress (SS), and rate of heat transfer (RHT) graphically represented. Significant findings include an annex in milk momentum with higher modified Hartmann number and a debilitation with wider electrode widths. Temperature elevations are noted in both hybrid nano-milk (HNM) and nano-milk (NM), with increases in the Casson parameter enhancing SS, while higher frequency parameters reduce RHT. Additionally, an AI-powered approach using an artificial neural network (ANN) achieves remarkable prediction accuracies-98.465% and 99.772% in SS predictions, and an impeccable 100% and nearly faultless 99.556% accuracy in RHT predictions. The findings of this research are particularly relevant in the dairy industry and other food processing sectors, where the quality and safety of products are paramount.</p>