<p>Precise control of thermal and hydrodynamic properties in complex fluids is critical for advancing industrial processes like dairy pasteurization, where efficiency and product quality are paramount. This study investigates the time-affected flow trait of milk enhanced with silver and magnesium oxide nanoparticles in an electromagnetically actuated channel under nonlinear thermal gradients, aiming to establish an intelligent control framework. The developed framework integrates key physical mechanisms, including thermal radiation, heat sink, and Darcy porous drag effects. A combination of analytical and computational techniques, such as the Laplace transform (LT) method, is employed to solve time-dependent flow equations efficiently. The analysis focuses on critical flow variables and metrics, with results visualized through comprehensive graphical and tabular representations. Findings indicate that higher modified Hartmann number amplifies milk momentum, whereas increased electrode widths counteract this effect. Hybrid nano-milk flow system exhibit intensified the shear stress (SS) with rising pressure oscillation frequencies, while greater heat source parameters elevate the rate of heat transfer (RHT). The LT generated dataset trains a Python-based artificial neural network (ANN) regressor, creating a fast-executing digital twin for real-time prediction. The ANN model demonstrates exceptional predictive accuracy, achieving up to 99.974% for SS and 99.93% for RHT on test data. Beyond advancing dairy processing techniques (e.g., pasteurization and sterilization), this work has significant implications for industries requiring precise thermal-fluid control. This AI-driven approach not only advances the fundamental understanding of nanofluid dynamics but also provides a transformative tool for enhancing precision, energy efficiency, and quality in dairy processing and related thermal-fluid control applications.</p>