In this research, a virtual metrology (VM) system is proposed to overcome those mentioned problems. It not only fulfills real-time quality measurement of each wafer, but also detects the performance degradation of the corresponding machines from the information of manufacturing processes. This paper makes four critical contributions: (1) The more principal component analysis (PCA) we used, the higher the accuracy we obtained by the kernel function approach; (2) the more support vector data description (SVDD) we used, the higher the accuracy we obtain in novelty detection module; (3) our empirical results show that genetic algorithm (GA) and incremental learning methods increase the training/learning of support vector machine (SVM) model; and (4) by developing a wafer quality prediction model, the SVM approach obtains better prediction accuracy than the radial basis function neural network (RBFN) and back-propagation neural network (BPNN) approaches. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential scrapped substrates risk of semiconductor manufacturing companies.