Abstract— An artificial neural network (ANN) approach was successfully deployed to model and predict the thermal conductivity of milk and concentrated milk systems during mechanized khoa production. Reliable data points (n=203) spanning wide operational boundaries of temperature (275.15–353.10 K), moisture content (48.82–92.00%), and fat content (0.00–11.17%) were compiled from established experimental studies to formulate and validate the model. A multi-layer feed-forward network optimized via the quasi-Newton algorithm using a 3:3:1 topology (three inputs, three hidden neurons with hyperbolic tangent activation functions, and a single linear output layer) demonstrated optimal predictive behaviour. The architecture yielded outstanding precision on independent testing subsets, demonstrating a strong correlation coefficient (R = 0.986), a minimal root mean squared error (RMSE = 0.0084 W/m•K), and a normalized squared error of 0.029 (normalized to the variance of the target data). Input sensitivity computations verified that product temperature (31.4% contribution) and moisture content (30.2% contribution) exert the highest thermodynamic control on thermal conductivity shifts, whereas fat content (4.4% contribution) exhibits a weaker but consistently inverse linear relationship. The resolved predictive equations were effectively embedded within a highly practical Microsoft Excel-based graphical user interface (GUI) to assist dairy process designers in real-time calculation, simulation, and industrial scaling of continuous scraped surface heat exchangers for indigenous milk confectionery manufacturing.