Plants within the Brassicaceae family, including oilseed rape, cabbage, and broccoli, are frequently subjected to intensive pesticide applications to mitigate losses caused by pests and pathogens. Such widespread chemical usage not only increases production costs but also poses significant risks to human health and the surrounding environment, highlighting the importance of alternative, sustainable crop protection strategies. The defence system of these species, primarily mediated by glucosinolates (GLSs) and their breakdown products, represents a promising natural means of pest management. A more detailed understanding of the regulatory mechanisms governing GLS biosynthesis may further enhance the ability to exploit these pathways for sustainable crop protection. In this context, an in silico analysis was undertaken to investigate the potential of computational predictions for deepening insights into the GLS pathway. Sequence conservation and protein–protein interaction (PPI) networks of central GLS enzyme families, MAM, CYP79, CYP83, and MYR, were analysed across multiple Brassicaceae genotypes. Established bioinformatic methodologies, including Multiple Sequence Alignment and PPI network mapping via the STRING database, were employed to identify evolutionarily conserved regions and to propose putative regulatory hubs that may play a critical role in pathway regulation. The outcomes of this analysis provide a predictive scaffold, enabling the formulation of hypotheses regarding essential residues and functional interactions that may influence the stability and efficiency of the GLS biosynthetic pathway. Recognising the preliminary nature of these computational findings, a prospective experimental validation strategy is outlined, incorporating site-directed mutagenesis and targeted metabolite profiling. Such insights are intended to support future experimental studies aimed at strengthening crop resistance through rational metabolic engineering and promoting sustainable, biologically based pest management strategies.