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At the plasma membrane, in response to biotic and abiotic cues, specific ligands initiate the formation of receptor kinase heterodimers, which regulate activities of plasma membrane proteins and initiate signaling cascades to the nucleus. In this study, we utilized affinity enrichment mass spectrometry (AE-MS) to investigate the stimulus-dependent interactomes of LRR receptor kinases in response to their respective ligands, with an emphasis on exploring structural influences and potential cross-talk events at the plasma membrane. BRI1 and SIRK1 were chosen as receptor kinases with distinct coreceptor preference. By using interactome characteristic of domain-swap chimera following a gradient boosting learning algorithm trained on SIRK1 and BRI1 interactomes, we attribute contributions of extracellular domain, transmembrane domain, juxtamembrane domain and kinase domain of respective ligand-binding receptors to their interaction with their coreceptors and substrates. Our results revealed juxtamembrane domain as major structural element defining the specific substrate recruitment for BRI1 and extracellular domain for SIRK1. Furthermore, the learning algrorithm enabled us to predict the phenotypic outcomes of chimeric receptors based on different domain combinations, which was verified by dedicated experiments. As a result, our work reveals a tightly controlled balance of signaling cascade activation dependent on ligand-binding receptors domains and the internal ligand status of the plant. Moreover, our study shows the robust utility of machine learning classification as a quantitative metric for studying dynamic interactomes, dissecting the contribution of specific domains and predicting their phenotypic outcome.