A method for estimating the state of wear of a tire of a mounted assembly of a vehicle traveling on a surface of a road, comprising the following steps: - measuring a vibroacoustic signal produced by the tire running on the road surface during a given time frame; - converting the time signal into a frequency signal over a given frequency range; - segmenting the frequency range into at least one frequency band with a predetermined width and associating at least one datum representing the frequency signal in said at least one frequency band with the at least one frequency band, with the at least one representative datum derived from the measurement forming at least one variable of a matrix associated with said measurement; - predicting a state of wear of the tire corresponding to the matrix associated with the completed measurement, by means of machine learning from the data based on a learning database made up of a set of matrices associated with measurements recorded and carried out according to the same steps as above, under known running conditions, according to modalities each representing a state of wear of the tire; - determining a ground surface state category; and wherein the ground surface state category is a condition for predicting the state of wear of the tire by the machine learning step if it is a specific ground state category containing the "dry" ground surface state category or the "dry" and "wet" ground surface state categories; - determining a ground nature category; and wherein the ground nature category is a condition for predicting the state of wear of the tire by the machine learning if it is a specific ground nature category comprising the ground nature category referred to as "open"; and - determining the state of wear of the tire after a number N of identical predictions in a series M of consecutive predictions. - measuring a vibroacoustic signal produced by the tire running on the road surface during a given time frame; - converting the time signal into a frequency signal over a given frequency range; - segmenting the frequency range into at least one frequency band with a predetermined width and associating at least one datum representing the frequency signal in said at least one frequency band with the at least one frequency band, with the at least one representative datum derived from the measurement forming at least one variable of a matrix associated with said measurement; - predicting a state of wear of the tire corresponding to the matrix associated with the completed measurement, by means of machine learning from the data based on a learning database made up of a set of matrices associated with measurements recorded and carried out according to the same steps as above, under known running conditions, according to modalities each representing a state of wear of the tire; - determining a ground surface state category; and wherein the ground surface state category is a condition for predicting the state of wear of the tire by the machine learning step if it is a specific ground state category containing the "dry" ground surface state category or the "dry" and "wet" ground surface state categories; - determining a ground nature category; and wherein the ground nature category is a condition for predicting the state of wear of the tire by the machine learning if it is a specific ground nature category comprising the ground nature category referred to as "open"; and - determining the state of wear of the tire after a number N of identical predictions in a series M of consecutive predictions.