This paper presents a method for analyzing the UV light aging time of inkpads using infrared spectroscopy coupled with machine learning techniques. A spectral database reflecting various degrees of aging was established, and the spectral data were refined using multiplicative scatter correction (MSC), standard normal variate transform (SNV), and Savitzky-Golay convolution smoothing (SG) to enhance the signal-to-noise ratio. An AdaBoost regression model for predicting the UV light aging time of inkpads was developed, with its parameters optimized through a grid search approach. This optimized model was benchmarked against support vector regression, random forest regression, and gradient boosting regression models. The study found that the SNV preprocessed infrared spectrum yielded the most accurate modeling results, outperforming those preprocessed with MSC and SG. The AdaBoost algorithm performed optimally with a decision tree depth of 4 and when the number of trees was 50 or more. As the decision tree depth increased, the optimized model required fewer trees. The AdaBoost model achieved perfect scores in mean square error, relative absolute error, coefficient of determination, and explainable variance, all of which were significantly better than the comparative algorithms.