A color prediction method for predicting a color obtained by applying a spot color ink on a process color ink, the color prediction method comprising: a learning step (S110, S210, S310) of performing machine learning using a plurality of pieces of teaching data including color component values for the process color ink and spectral characteristics corresponding to the color component values by a neural network that takes color component values as input data and takes a spectral characteristics as output data; an inference step (S130, S220, S330) of giving, as input data, color component values of a processing-target process color ink which is a process color ink constituting a prediction target color to a learned neural network obtained in the learning step (S110, S210, S310) so as to obtain spectral characteristics for the processing-target process color ink; a correction step (S140, S240, S350) of correcting the spectral characteristics obtained in the inference step (S130, S220, S330); and a prediction step (S150, S250, S360) of obtaining, based on the spectral characteristics after correction by the correction step (S140, S240, S350) and spectral characteristics for a processing-target spot color ink which is a spot color ink constituting the prediction target color, spectral characteristics in a state where the processing-target spot color ink is applied on the processing-target process color ink, characterized in that, in the correction step (S140, S240, S350), the spectral characteristics after the correction are obtained such that a value of an objective function is minimized, the objective function being represented by a following equation: L = C1 · Δ X + C2 · Δ Y + C3 · Δ Z + C4 · ΔS where L represents the objective function, ΔX, ΔY, and ΔZ represent values determined according to respective differences between tristimulus values X, Y, and Z of the processing-target process color ink and tristimulus values X, Y, and Z corresponding to the spectral characteristics after the correction, ΔS represents a sum of squares of differences between the spectral characteristics before the correction and the spectral characteristics after the correction, and C1, C2, C3, and C4 represent coefficients. a learning step (S110, S210, S310) of performing machine learning using a plurality of pieces of teaching data including color component values for the process color ink and spectral characteristics corresponding to the color component values by a neural network that takes color component values as input data and takes a spectral characteristics as output data; an inference step (S130, S220, S330) of giving, as input data, color component values of a processing-target process color ink which is a process color ink constituting a prediction target color to a learned neural network obtained in the learning step (S110, S210, S310) so as to obtain spectral characteristics for the processing-target process color ink; a correction step (S140, S240, S350) of correcting the spectral characteristics obtained in the inference step (S130, S220, S330); and a prediction step (S150, S250, S360) of obtaining, based on the spectral characteristics after correction by the correction step (S140, S240, S350) and spectral characteristics for a processing-target spot color ink which is a spot color ink constituting the prediction target color, spectral characteristics in a state where the processing-target spot color ink is applied on the processing-target process color ink, characterized in that, in the correction step (S140, S240, S350), the spectral characteristics after the correction are obtained such that a value of an objective function is minimized, the objective function being represented by a following equation: L = C1 · Δ X + C2 · Δ Y + C3 · Δ Z + C4 · ΔS where L represents the objective function, ΔX, ΔY, and ΔZ represent values determined according to respective differences between tristimulus values X, Y, and Z of the processing-target process color ink and tristimulus values X, Y, and Z corresponding to the spectral characteristics after the correction, ΔS represents a sum of squares of differences between the spectral characteristics before the correction and the spectral characteristics after the correction, and C1, C2, C3, and C4 represent coefficients.