As global seafood trade continues to expand and supply chains become increasingly complex, there is a growing need for rapid and non-destructive methods to assess seafood quality and safety. However, water absorption,Mie scattering, endogenous fluorescence, overlapping spectral features and batch-to-batch variation in real seafood matrices can compromise the robustness of conventional linear models. This review examined a range of spectroscopic and imaging techniques, including Raman spectroscopy and surface-enhanced Raman scattering (SERS), mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), Terahertz spectroscopy (THz), ultraviolet-visible absorption spectroscopy (UV-Vis), molecular fluorescence spectroscopy (MFS), and hyperspectral imaging (HSI). It summarized the applications of artificial intelligence (AI) in freshness assessment, species authentication, detection of freeze-thaw damage, screening for residues and toxins, and microbial risk assessment in seafood. It focused on the roles of AI in nonlinear modeling, complex pattern recognition and spatial information extraction, and discussed issues such as model generalizability, lightweight deployment and confirmation by standard methods. This paper can serve as a reference for the rational selection, performance evaluation and practical application of spectroscopic techniques and AI models in the rapid screening of seafood quality and safety.