Background and Objectives A high amount of the world wheat production is used for bread making. Due to its simplicity, wheat samples are traded based on protein content, although this is only an indirect measurement of baking quality. As direct bread quality traits like loaf volume, water absorption, and dough properties are cumbersome to measure along wheat supply chains and in wheat breeding programs, this study aims to predict directly dough properties and baking behavior by combining complementary information from near‐infrared, Raman, and fluorescence spectroscopy. Several proven data preprocessing and regression algorithms were evaluated, including partial least‐squares regression (PLSR) and genetic algorithm (GA). Findings A highly diverse sample set of 415 wheat samples from a wheat hybrid breeding program was analyzed as whole grain, whole grain flour, and extracted flour. The best predictive models yielded a 10‐fold cross‐validated coefficient of determination R2 of .973, .873, .774, .835, .369, .447, .311, .512, .536, .723, and .715 for protein content, wet gluten, loaf volume, water uptake, elongation resistance to extension, extensibility, ratio number, extensograph energy, maltose number, plant height, and grain yield, respectively. Conclusion The prediction accuracies are well suited for screening purposes in wheat breeding and allow better quality assessment compared to protein content along wheat supply chains. Significance and Novelty In this study, complementary spectroscopic techniques are combined for the first time to achieve higher prediction accuracy for a wide range of wheat quality parameters. In addition, Raman and fluorescence spectroscopy were used to predict quality data from a large sample set.