In this study, a machine tool operating vibration prediction method based on multi-sensor fusion and long short-term memory (LSTM) network is proposed. Machine tool vibration has a significant impact on machining quality, workpiece surface roughness, dimensional accuracy, and tool’s wear. This study combines deep learning technology with industrial applications to achieve high-precision machine tool vibration prediction by fusing multiple sensor data. The real-time data is input into the LSTM model to predict the vibration situation at the next moment. The experimental results show that the method has strong prediction ability for the periodic vibration of the machine tool and the vibration error specific to the machining action. And it can effectively predict machine vibration and improve machining accuracy.