<p>Roots (lobe) blowers, essential positive displacement machines in various industries, experience efficiency losses due to increased clearance between fixed and moving parts, leading to backflow—where fluid reverts from the high-pressure outlet to the low-pressure inlet. This study investigates the feasibility of backflow detection through three innovative methodologies grounded in industrial data, despite previous research largely relied on laboratory datasets and simulations. The methods examined include ultrasound testing enhanced by a novel post-processing algorithm, thermography, and a machine learning approach trained on experimental data. Data were collected over one year from seven industrial blowers in a steel company’s DRI (Direct Reduced Iron) factory. This research primarily focuses on the challenges associated with multiple blowers configured in series or parallel arrangements, a topic that has been less explored. Main focus of this research is in multiple blowers in serial/parallel configurations where it is not known in practical which blower has backflow problem while facing reduction in flow rate and efficiency of the plant. Results demonstrate that the ultrasonic technique, combined with a defined mathematical feature, effectively detects backflow. In contrast, thermography was found to be impractical, primarily due to water injection at the blower inlet- a factor that had not been previously addressed in theoretical studies. Furthermore, the employed machine learning model, a support vector machine (SVM), which is the first step in making the backflow detection in blowers intelligent, proves capable of intelligent backflow detection. Validation is conducted through an innovatively designed experiment measuring electric current and output pressure in a genuine industrial environment. This research offers practical insights for optimizing blower performance and ensuring operational efficiency by detecting backflow in serial and parallel blowers.</p>