An overview is provided of the research progress in the application of hyperspectral detection technology for non-destructive testing of key parameters in tobacco leaf quality. Methods and equipment for the rapid detection of chemical components such as total sugar, reducing sugar, total nitrogen, nicotine, starch, chloride, and potassium in tobacco leaves using this technology are explored. The impact of different tobacco sample forms on spectral data is pointed out. The advantages and challenges of hyperspectral technology in applications such as field management, harvest optimization, and online grading in tobacco production are analyzed. The promising prospects of combining hyperspectral technology with artificial intelligence to build predictive models for tobacco leaf chemical composition are proposed. This combination provides scientific evidence and references for improving detection efficiency and quality in the tobacco industry.