The baking process in tunnel ovens can be influenced by many parameters. Among these, the most important can be considered as: the baking time, the volume of dough pieces, the texture and humidity of the dough, the distribution of temperature inside the oven, as well as the flow of air currents applied in the baking chamber. In order to obtain a constant quality of bakery or pastry products, and for the efficient operation of the oven, it is necessary that the solution made by the designers be subjected to modelling, simulation and analysis processes, before their manufacture, and in this sense it can be applied to the Computational Fluid Dynamics (CFD) numerical simulation tool. In this study, we made an analysis of the air flow inside the baking chamber of an oven. The analyzed oven was used very frequently on the pastry lines. After performing the modelling and simulation, the temperature distribution inside the oven was obtained in the longitudinal and transverse planes. For the experimental validation of the temperatures obtained in the computer-assisted simulation, the temperatures inside the analyzed electric oven were measured. The measured temperatures validated the simulation results with a maximum error of 7.6%.

Sustainable food systems are increasingly challenged by climate change, resource constraints, market volatility, and growing food demand, making accurate forecasting of food production essential for food security and long-term sustainability. Despite the growing use of machine learning in agricultural forecasting, studies directly modeling the Food Production Index (FPI) within a sustainable food systems framework remain limited, particularly in emerging economies. This study addresses this gap by forecasting Türkiye’s Food Production Index using agricultural, macroeconomic, and trade-related indicators covering the period 1962–2023. Seven predictive approaches, including Multiple Linear Regression (MLR), Bayesian Ridge Regression, Support Vector Regression (SVR), Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), and K-Nearest Neighbors (KNN), were comparatively evaluated using R², RMSE, and MAE metrics. The results demonstrate that Bayesian Ridge Regression (R² = 0.968) and MLR (R² = 0.918) significantly outperform more complex machine learning algorithms, indicating that model–data compatibility is more critical than algorithmic complexity in long-term food production forecasting. The findings reveal that economic growth, agricultural inputs, and structural transformation processes play a decisive role in shaping food production dynamics. By integrating machine learning with sustainability-oriented food system analysis, this study provides a robust evidence base for supporting food security strategies, resource-efficient agricultural planning, and resilient food system governance. The proposed framework offers macro-level decision-support insights for policymakers engaged in long-term food system planning, strategic risk monitoring, and evidence-based policy evaluation.