Abstract: Nuclear magnetic resonance spectroscopy (NMR) is a pivotal analytical method in organic chemistry and biochemical studies. As a potential metrological reference method, quantitative NMR has become the primary approach for value assignment and international comparison of purity reference materials for organic compounds and biochemical macromolecules. In recent years, the integration of artificial intelligence (AI) into NMR spectroscopy has vastly changed this field, significantly enhancing analytical accuracy, efficiency, and applicability. This review summarizes global research progress in AI-assisted NMR spectroscopy over the past decade. Key advancements include AI applications in chemical shift prediction, spectrum simulation and reconstruction, peak selection and spectral processing, shimming, radio-frequency pulse design, structural elucidation of pure compounds, reaction monitoring, complex matrix sample analysis (e.g., marine, metabolomic and biomolecular analysis), material property prediction, and industrial quality control. Additionally, dedicated AI technologies tailored for NMR applications are discussed.