Drum-type flying shears represent a technological system used in material processing lines (MPL). During operation, they must withstand an impact shear torque of very short duration with an amplitude comparable to the rated motor torque. The position of the shears is derived from the strip speed, meaning that any change in strip speed is directly reflected in the shears’ speed. Therefore, the primary control task is to ensure precise synchronization between the strip speed and the shears at the moment cutting begins, as well as to minimize the speed drop of the shears during the cutting process. This prevents deformation of the cut strip and deterioration of the dimensional accuracy of the cut sheets. Previous research has shown that conventional control methods supplemented with shear torque compensation can significantly improve speed stability during cutting. Building on these findings, this paper analyzes the application of artificial intelligence elements to optimize the control of flying drum shears. The study compares traditional control approaches with methods based on fuzzy logic and neural networks, aiming to further improve system dynamics and minimize speed deviations during cutting. The proposed control structures and compensations are verified through simulation, and the results are presented.