In order to explore the feasibility of detecting wheat kernel black tip (BT) disease and investigating an optimized classification model based on mainstream machine learning algorithms, a large amount of 2 760 wheat kernels spectral data of Vis/NIR bands (579~1 099 nm) were collected by self-made spectral acquisition platform. After pretreated with standard normal variate correction (SNV) of 600~1 045 nm bands, 7 kinds of data sets were established. Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) of spectral data dimensionality reduction methods, and four machine learning algorithms, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Random Forest (RF) and AdaBoost, were adopted to develop eight classification models. Results showed that Vis/NIR spectrums combined with all the machine learning methods could be used to detect BT disease with accuracies ranging from 93.3% to 98.6%, which indicated that Vis/NIR would be the more effectively compared to NIR. As SPA-SVM possessed a high average classification accuracy and PCA-AdaBoost showed better generalization performance than other algorithms, considering practical purposes, these two algorithms were adopted as optimized models in 2-category classification, 3-category classification and 4-category classification for various degrees of BT detection. Results indicated that the classification accuracies declined gradually with the classification number increasing, but the detection accuracy of non-diseased wheat kernel tended to be stable with an accuracy of more than 87.2%. Taken together, SPA-SVM performed better than PAC-AdaBoost in wheat BT disease detection. The models and conclusions of this research are intended to lead to the streamlining of VIS/NIR spectroscopy in automated wheat black tip inspection as well as to provide criteria for high speed sorting.