Despite its disadvantages, distillation is the chemical industry’s most frequently used separation method. However, it consumes much energy and does not often produce the high purity of bioethanol. This study illustrates a new approach to improving the efficiency of bioethanol production through complex mixtures of water, ethanol, and contaminants to overcome its inconveniences. The hybrid method was used to optimize the operating conditions of the industrial distillation process, such as the reflux ratio, feeding tray, and column pressure. This approach merges the simulation of the distillation process using central composite design with deep learning and genetic algorithms. This integrated methodology provides a more precise and practical assessment of the operation conditions, such as the reflux ratio, pressure, and feeding column, resulting in improved process performance and operating costs. The results show a significant reduction of approximately 52% in energy consumption and costs. The applied methodology not only improves the performance of the industrial distillation process but also offers a sustainable method with potential applications in other operation units.