Method of training at least one machine learning artificial intelligence model (50, 51, 52), said at least one machine learning artificial intelligence model (50, 51, 52) being configured to be stored in a memory equipping an aircraft or a ground station and developed to be implemented during at least one predetermined flight phase of at least one aircraft of the same type, said method comprising conducting a plurality of flights, for at least one flight among said plurality of flights, said method comprising acquiring in-flight at least one set of flight data (J1, J2) acquired with several sensors (3, 3') at the same point in time t, characterized in that, for said at least one flight among said plurality of flights, said method comprises the following steps: • conducting at least one predetermined consistency test with at least one consistency controller (5, 15), said at least one consistency test making it possible to check that a reliable reference weight (Mref) is calculated or capable of being calculated, • calculating with at least one weight calculation controller (5', 15') at least one calculated weight (Mc1, Mc2) of said aircraft based on at least one weight chosen from the group comprising a current boarded fuel weight, a consumed fuel weight, a previously measured weight of said aircraft, an empty weight of said aircraft, a payload previously input by a user, a weight measured by connected equipment and said reference weight (Mref), • if said at least one consistency test is validated, storing said at least one set of flight data (J1, J2) and said at least one calculated weight (Mc1, Mc2) at said point in time t, each stored set of flight data (J1, J2) associated with a calculated weight (Mc1, Mc2) forming a training data set ({J1, Md}, {J2, Mc2}) for said at least one machine learning artificial intelligence model (50, 51, 52), and in that said method comprises using said training data sets ({J1, Mc1}, {J2, Mc2}) to program said at least one machine learning artificial intelligence model (50, 51, 52), said at least one machine learning artificial intelligence model (50, 51, 52) being configured to estimate at any time an estimated instantaneous weight (Mi) of said aircraft, or another aircraft of the same type, from a current set of flight data (Jc). said method comprising conducting a plurality of flights, for at least one flight among said plurality of flights, said method comprising acquiring in-flight at least one set of flight data (J1, J2) acquired with several sensors (3, 3') at the same point in time t, characterized in that, for said at least one flight among said plurality of flights, said method comprises the following steps: • conducting at least one predetermined consistency test with at least one consistency controller (5, 15), said at least one consistency test making it possible to check that a reliable reference weight (Mref) is calculated or capable of being calculated, • calculating with at least one weight calculation controller (5', 15') at least one calculated weight (Mc1, Mc2) of said aircraft based on at least one weight chosen from the group comprising a current boarded fuel weight, a consumed fuel weight, a previously measured weight of said aircraft, an empty weight of said aircraft, a payload previously input by a user, a weight measured by connected equipment and said reference weight (Mref), • if said at least one consistency test is validated, storing said at least one set of flight data (J1, J2) and said at least one calculated weight (Mc1, Mc2) at said point in time t, each stored set of flight data (J1, J2) associated with a calculated weight (Mc1, Mc2) forming a training data set ({J1, Md}, {J2, Mc2}) for said at least one machine learning artificial intelligence model (50, 51, 52), • conducting at least one predetermined consistency test with at least one consistency controller (5, 15), said at least one consistency test making it possible to check that a reliable reference weight (Mref) is calculated or capable of being calculated, • calculating with at least one weight calculation controller (5', 15') at least one calculated weight (Mc1, Mc2) of said aircraft based on at least one weight chosen from the group comprising a current boarded fuel weight, a consumed fuel weight, a previously measured weight of said aircraft, an empty weight of said aircraft, a payload previously input by a user, a weight measured by connected equipment and said reference weight (Mref), • if said at least one consistency test is validated, storing said at least one set of flight data (J1, J2) and said at least one calculated weight (Mc1, Mc2) at said point in time t, each stored set of flight data (J1, J2) associated with a calculated weight (Mc1, Mc2) forming a training data set ({J1, Md}, {J2, Mc2}) for said at least one machine learning artificial intelligence model (50, 51, 52), and in that said method comprises using said training data sets ({J1, Mc1}, {J2, Mc2}) to program said at least one machine learning artificial intelligence model (50, 51, 52), said at least one machine learning artificial intelligence model (50, 51, 52) being configured to estimate at any time an estimated instantaneous weight (Mi) of said aircraft, or another aircraft of the same type, from a current set of flight data (Jc).