Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes the functional understanding of molecular plant stress response compromising the design of breeding strategies and biotechnological processes. Thus, defining a molecular network to enable the prediction of a plant’s stress mode promises to promote the understanding of stress responsive biochemical regulation and its technological application. Arabidopsis wild type plants and two mutant lines with deficiency in sucrose or starch metabolism were grown under ambient and cold/high light stress conditions. Stress-induced dynamics of the primary metabolome and the proteome were quantified in a mass spectrometry-based high-throughput experiment. Wild type data were used to train a machine learning algorithm to classify mutant lines under control and stress conditions. Multivariate analysis and classification identified a module consisting of 23 proteins enabling the reliable prediction of coupled temperature and light stress conditions. 18 of these 23 proteins displayed putative protein-protein interactions connecting transcriptional regulation with regulation of primary and secondary metabolism under stress. The identified stress-responsive core module provides evidence for predictability of complex biochemical regulation during environmental fluctuation.