Abstract: The present disclosure relates to a method and apparatus for non-invasive estimation of glycated hemoglobin (HbA1c) or blood glucose by using machine learning, the method comprising: a sig nal collection stage of collecting a bio-signal of a measurement subject to be measured; a feature extraction stage of extracting a plurality of features from the bio-signal; a machine learning model construction stage of constructing a machine learning model for estimating glycated hemoglobin or blood glucose by learning training data including the plurality of features; and a glycated hemoglobin/blood glucose estimation stage of generating input data on the basis of the bio-signal extracted from the measurement subject being measured and inputting the input data to the machine learning model, so as to estimate glycated hemoglobin or blood glucose of the measurement subject being measured.