Integration of large language models (LLMs) into analytical chemistry provides a more efficient solution for handling complex analytical workflows and exponentially growing datasets. In this review, we summarize the capabilities, applications, and limitations of LLMs in handling spectral interpretation, predictive modeling, and experimental automation. Firstly, we systematically analyze the relevant literature and case studies in recent years, and then we evaluate the performance of LLMs in handling multimodal data (such as spectroscopy, text processing, and numerical input), the adaptability of LLMs to specific domain tasks such as automated spectral analysis, environmental monitoring, and real-time decision support, and also the possibility of synergy with traditional computing tools. The results show that LLMs possess obvious versatility in spectral chemical state recognition and environmental parameter prediction, and have similar accuracy to the traditional machine learning models. Although LLMs show great potential in analytical chemistry data processing, robust validation protocols, and ethical governance, LLMs still need to be further improved in contextual reasoning, data security, and interpretability, which require human cross-validation. Interdisciplinary collaboration to improve LLMs architecture and enhance visualization operations possesses huge potential in addressing scalability issues in scientific research applications.