The accurate estimation of blood age is of great significance in the forensic identification of criminal investigation. In this paper, a visible-near-infrared multispectral imaging system with 8 LEDs as illumination source and monochrome CCD camera as image input unit is constructed. The ensembling model based on k nearest neighbor method, support vector machine and random forest method is used to analyze and estimate the age of bloodstains. The feasibility of using the visible-near-infrared reflectance multispectral to accurately estimate the age of human blood was investigated, and the results were compared with the previous studies using hyperspectral techniques for blood age estimation. The influence of blood specificity was also tested. The experiment recorded images of 8 channels from 400 to 940 nm on days 1 to days 20 of 11 human blood samples, and the spectra were preprocessed using standard normal variate transformation (SNV) to eliminate spectral differences due to the baseline shift and scattering. Seven preprocessed samples were randomly selected as training set to build the model, and the remaining four samples were used as test sets to test the model, a model ensembling model based on k nearest neighbor method, support vector machine and random forest method was built. Compared with the results by k-NN model, SVM model and RF model the result is better. The correct classification rate (CCR) of the samples between 0 and 2 d is 80%, the average error is 0.053 d, and the CCR between 2 and 20 d is 69%. The average error is 0.442 d, which is comparable or better than that obtained by using hyperspectral techniques. In order to test the practical applicability of the method, this paper tested the effect of blood specificity on the model. The test sample was 20 blood samples taken from 8 different donors, 10 of which from 4 donors were used to refine the original model, and 10 samples from another 4 donors were used as test sets to test the effect of blood specificity. The estimated age of blood from different sources is: CCR is 75.6% between 0 and 2 d, and the average error is 0.063 1 d. After adding blood samples from different donors, there was no significant decrease in CCR, indicating that the model still has good adaptability to blood samples from different sources. The results show that compared with the previous research results, multispectral technology combined with model ensembling algorithm could obtain more accurate age estimation results, and has the advantages of simple set-ups, low-cost and good stability, which might be a high-precision blood age estimation method and have important application value in the field of forensic science.