The traceability analysis of material evidence has always been a tough area of forensic expertise, which is crucial for criminal investigations. This study proposes a new method for the detection and traceability analysis of human fingernail samples using attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with machine learning related methods. The ATR-FTIR of fingernails from 195 volunteers were collected. These volunteers came from 18 provinces located in the 7 regions of China. The dimensionality of the original spectral data was reduced via principal component analysis (PCA) and factor analysis (FA) after preprocessing. Multilayer perceptron (MLP), radial basis function (RBF), decision tree (DT), and support vector machine (SVM) models were used for classification and recognition. The experimental results show that in the subsequent modeling analysis, there is little difference between PCA and FA. The classification effect of the MLP is better than that of the RBF. The classification accuracy of the training set and test set of the PCA-DT model based on the CHAID algorithm can reach 91.0% and 92.0%, respectively, which are better than those of the exhaustive CHAID, CRT, and QUEST algorithms. PCA-SVM model based on the polynomial kernel function can fully distinguish fingernail samples from seven regions and five provinces in North China. Its classification accuracy is better than those of RBF, Sigmoid, and linear kernel functions. Therefore, the ATR-FTIR technology combined with the PCA-SVM model can accurately classify the fingernail samples from different regions. This study establishes a new method reference for analyzing the traceability of fingernail evidence.