Abstract: Hyperspectral imaging (HSI) technology integrates imaging and spectroscopy to simultaneously acquire spatial and spectral information of target samples at the pixel level, emerging as a crucial non-destructive testing method in food and agricultural analysis. Although extensive research has been conducted on the application of HSI throughout the tobacco industry chain, systematic reviews remain relatively scarce. Therefore, this paper provides a comprehensive review of HSI applications in the tobacco industry, covering three major stages: field management, raw material quality evaluation, and quality control during processing. In field management, HSI is primarily utilized for the dynamic monitoring of crop growth and nutrition, early warning of diseases, and maturity analysis. For raw material quality evaluation, this technology is employed in the quantitative analysis of chemical components, characterization of physical properties, early identification of mildew, and intelligent classification of attributes such as stalk position, grade, and variety. In the realm of processing control, HSI demonstrates significant technical advantages in quantifying the blending ratio of cut tobacco, evaluating the uniformity of casing and flavoring, online monitoring of moisture content, and detecting and removing foreign matter. Finally, this paper analyzes the primary challenges facing current research, including limited dataset scales, insufficient cross-scenario generalization capabilities of models, and the barriers to scale migration from the laboratory to the field or production line. Addressing these issues, this paper further outlines the future development trends and research directions in this field.