Efficient management of industrial air conditioning systems represents a critical challenge in the food industry, where environmental control directly impacts both product quality and energy consumption. This work presents an approach based on Support Vector Machines (SVM) for predicting favorable conditions for the incorporation of outdoor air, aiming to reduce the use of active climatization without compromising the required environmental parameters. More than 71,000 on-site environmental data records were used to build and validate an SVM model capable of classifying, with 99.1% accuracy, the appropriate moments to deactivate the HVAC system. The proposed method is empirically contrasted with the performance of a real industrial system governed by PID control, showing that the SVM model offers superior anticipatory capabilities in response to external variations, with a projected energy savings of 10% to 18%. The results position the SVM model as a viable, robust, and scalable alternative for real-world industrial applications. Finally, action lines are proposed for hybrid implementation and extension of the model toward continuous learning schemes.