The invention relates to a method for predicting flooding in a distillation column by machine learning including a constructing and training phase of a machine learning model obtained from previously collected data and from a set of sensors, an operational phase for predicting flooding(s), by collecting a current data flow until a buffer is filled, pre-processing data from the data buffer by predetermined cleansing and classification, synchronizing the data of the current set of clean and classified data, determining a value of a current variable representative of at least one current performance of the at least one distillation column, forming a current set of transformed data by calculating predetermined derivatives, and predicting the current state of said distillation column by applying said learning model to said current set of transformed data.