A method for predicting flooding in at least one distillation column of a refinery, as implemented by machine learning, the method comprising: - a constructing and training phase of a machine learning model for predicting flooding, as obtained from a set of data collected beforehand, during a predetermined prior period, and at least from a set of sensors of the refinery, comprising sensors adapted to measure a plurality types of data within the distillation column considered, each collected datum being associated with a sensor time datum, - an operational phase for predicting flooding comprising the following steps: - from said set of sensors of the refinery, collecting a current data flow until a data buffer of predetermined size is filled, each datum of the current data flow being further associated with a sensor time datum, - pre-processing the data from the data buffer by predetermined cleansing and classification delivering a current set of clean and classified data, - synchronizing the sensor time data associated with the data of the current collected data flow of the current set of clean and classified data, delivering a current set of clean, classified and synchronized data, - from the current set of clean, classified and synchronized data, determining at least one current value of a variable representative of at least one current performance of the at least one distillation column, and adding the at least one value of the variable to the current set of clean, classified and synchronized data so as to form a current set of data to be processed, - forming a current set of transformed data by calculating predetermined derivatives of the current set of data to be processed, - predicting the current state of said at least one distillation column by applying said learning model to said current set of transformed data, said current state corresponding to a binary value representative of either the presence or the absence of a current pre-flooding. - a constructing and training phase of a machine learning model for predicting flooding, as obtained from a set of data collected beforehand, during a predetermined prior period, and at least from a set of sensors of the refinery, comprising sensors adapted to measure a plurality types of data within the distillation column considered, each collected datum being associated with a sensor time datum, - an operational phase for predicting flooding comprising the following steps: - from said set of sensors of the refinery, collecting a current data flow until a data buffer of predetermined size is filled, each datum of the current data flow being further associated with a sensor time datum, - pre-processing the data from the data buffer by predetermined cleansing and classification delivering a current set of clean and classified data, - synchronizing the sensor time data associated with the data of the current collected data flow of the current set of clean and classified data, delivering a current set of clean, classified and synchronized data, - from the current set of clean, classified and synchronized data, determining at least one current value of a variable representative of at least one current performance of the at least one distillation column, and adding the at least one value of the variable to the current set of clean, classified and synchronized data so as to form a current set of data to be processed, - forming a current set of transformed data by calculating predetermined derivatives of the current set of data to be processed, - predicting the current state of said at least one distillation column by applying said learning model to said current set of transformed data, said current state corresponding to a binary value representative of either the presence or the absence of a current pre-flooding. - from said set of sensors of the refinery, collecting a current data flow until a data buffer of predetermined size is filled, each datum of the current data flow being further associated with a sensor time datum, - pre-processing the data from the data buffer by predetermined cleansing and classification delivering a current set of clean and classified data, - synchronizing the sensor time data associated with the data of the current collected data flow of the current set of clean and classified data, delivering a current set of clean, classified and synchronized data, - from the current set of clean, classified and synchronized data, determining at least one current value of a variable representative of at least one current performance of the at least one distillation column, and adding the at least one value of the variable to the current set of clean, classified and synchronized data so as to form a current set of data to be processed, - forming a current set of transformed data by calculating predetermined derivatives of the current set of data to be processed, - predicting the current state of said at least one distillation column by applying said learning model to said current set of transformed data, said current state corresponding to a binary value representative of either the presence or the absence of a current pre-flooding.