AI‐Predicted Dynamics of Milk Enhanced With Silver and Zinc Oxide Nanoparticles for Electromagnetic Pasteurization and Sterilization

This study integrates artificial intelligence (AI) with nanofluid dynamics to predict and regulate the flow behavior of silver and zinc oxide nanoparticle‐enriched milk (Ag─ZnO/milk) subjected to Riga plate‐induced electromagnetic forcing under sudden pressure changes, targeting precision, energy efficiency, and optimal thermal control in dairy pasteurization and sterilization. The governing model incorporates thermal radiation and Darcy‐based porous drag resistance, with the resulting equations solved analytically via the Laplace transform (LT) method. Shear stress (SS) and the rate of heat transfer (RHT) are systematically evaluated and graphically presented, revealing that: (i) milk momentum is notably enhanced with increasing modified Hartmann numbers yet suppressed by broader electrode widths; (ii) temperature elevations are recorded in both hybrid nano‐milk (HNM) and nano‐milk (NM); (iii) higher Casson parameters augment SS, while stronger thermal radiation diminishes RHT. An artificial neural network (ANN) framework is further employed to evaluate SS and RHT, achieving predictive accuracy across both training and testing datasets—validating its reliability as a computational tool. The proposed model holds transformative potential for dairy industry applications, offering pathways toward improved taste, texture, nutritional quality, and sustainable processing practices.