Abstract: As quality and safety standards for aquatic products continue to tighten, and as intelligent processing becomes increasingly important, conventional methods such as chemical assays and sensory evaluation are proving insufficient for the needs of modern aquaculture and fish processing. On the one hand, chemical analyses are usually destructive, labor-intensive, and too slow for rapid or large-scale inspection. On the other hand, sensory evaluation depends heavily on human experience and subjective judgment, which limits its reliability in automated quality assessment. Under these circumstances, hyperspectral imaging (HSI) has emerged as an attractive nondestructive approach for evaluating fish quality. A major advantage of HSI lies in its ability to integrate imaging with spectroscopy, thereby capturing spatial and spectral information simultaneously from fish muscle. Covering the visible, near-infrared, and short-wave infrared regions, this technique can reveal a broad range of quality-related variations in fish tissues. Such variations may be linked to freshness, moisture distribution, lipid characteristics, microbial spoilage, and other physicochemical changes. When combined with spatial distribution patterns, spectral signals provide a more comprehensive basis for evaluating multiple quality attributes without causing damage to the sample. For this reason, HSI has shown clear superiority over conventional imaging methods and destructive analytical techniques in fish-quality detection. In this review, recent progress in the application of HSI to fish freshness, moisture, fat, microbial spoilage, and parasite contamination is systematically summarized. Emphasis is placed on the spectral ranges most frequently used in current research, approaches for selecting informative wavelengths, modeling methods, and their predictive performance. Meanwhile, attention is also given to the main barriers that still hinder the broader use of HSI. Among them are the lack of consistency in spectral acquisition systems and instrument standards, difficulties in data processing and result interpretation, insufficient adaptability to different sample-handling conditions and real application scenarios, limited multi-index fusion and model generalization, as well as high equipment costs and the absence of unified standards. Taken together, these issues reduce the comparability of existing studies and make it more difficult to translate laboratory findings into industrial practice. Looking ahead, future advances are likely to involve closer equipment integration, higher levels of intelligence, multimodal data fusion, closed-loop process control, and deeper application in fish-quality evaluation. At the same time, the establishment of standardized technical frameworks and more complete industrial systems will be essential for wider adoption. Further integration of HSI with other sensing technologies and intelligent decision-making tools may also improve detection efficiency, robustness, and practical applicability. Overall, this review is expected to offer a useful reference for advancing the intelligent upgrading of fish-product processing and strengthening the quality and safety assurance of aquatic products.