Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this article, we integrate random forest (RF) with Bayesian optimization for quality prediction with large-scale dimensions data, selecting crucial production elements by information gain, and then utilizing sensitivity analysis to maintain product quality. Horizontal empirical experiments are performed to verify the superiorities of RF embedded within Bayesian optimization over classical RF, support vector machine, logistic regression, decision tree, and even background propagation neural network. Besides, we find fewer but critical features handled by RF-Bayesian optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon's management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at the real-life process industry.