Continuous industrial ovens, such as conveyor ovens, are widely used in food, automotive, and coating industries for baking, drying, and curing processes. Accurate modeling of these systems is essential for predicting energy requirements, fuel consumption, and product quality indicators. Traditional physics-based models provide detailed insights into oven dynamics but often require complex formulations for product-specific behavior, leading to high computational costs. Conversely, pure machine learning (ML) models can capture nonlinear product responses but lack physical interpretability and may fail under unseen conditions. In this work, we present a hybrid modeling framework that integrates a physics-based oven model with a data-driven ML product model. The oven environment is simulated using a zone-based approach that predicts air temperature, humidity, and velocity distributions under varying operating conditions. The product behavior—such as internal temperature profiles and moisture content—is modeled using an artificial neural network (ANN) trained on experimental data, with oven conditions from the physics-based model serving as inputs. This coupling ensures that the ML component operates within physically realistic bounds while capturing complex, data-rich product dynamics. The proposed methodology combines the interpretability and robustness of first-principles modeling with the flexibility of ML, enabling accurate prediction of oven performance and product attributes. This approach offers significant advantages in process optimization, energy efficiency, and computational speed compared to traditional models, making it highly suitable for industrial applications where both accuracy and scalability are critical.