Cabbage (Brassica oleracea L.) is a globally significant vegetable crop that faces productivity challenges due to fungal and bacterial pathogens. This review highlights the potential of spectral imaging techniques, specifically multispectral and hyperspectral methods, in detecting biotic stress in cabbage, with a particular emphasis on pathogen-induced responses. These non-invasive approaches enable real-time assessment of plant physiological and biochemical changes, providing detailed spectral data to identify pathogens before visible symptoms appear. Hyperspectral imaging, with its high spectral resolution, allows for distinctions among different pathogens and the evaluation of stress responses, whereas multispectral imaging offers broad-scale monitoring suitable for field-level applications. The work synthesizes research in the existing literature while presenting novel experimental findings that validate and extend current knowledge. Significant spectral changes are reported in cabbage leaves infected by Alternaria brassicae and Botrytis cinerea. Early-stage detection was facilitated by alterations in flavonoids (400–450 nm), chlorophyll (430–450, 680–700 nm), carotenoids (470–520 nm), xanthophyll (520–600 nm), anthocyanin (550–560 nm, 700–710 nm, 780–790 nm), phenols/mycotoxins (700–750 nm, 718–722), water/pigments content (800–900 nm), and polyphenols/lignin (900–1000). The findings underscore the importance of targeting specific spectral ranges for early pathogen detection. By integrating these techniques with machine learning, this research demonstrates their applicability in advancing precision agriculture, improving disease management, and promoting sustainable production systems.