Artificial Intelligence in Spectroscopy and Analytical Chemistry: Enhancing Precision through Automation and Insight

The way chemists interpret and use complicated spectral data is changing as a result of the incorporation of Artificial Intelligence (AI) into spectroscopy and analytical chemistry. Conventional procedures for examining spectra from methods like Ultraviolet-Visible (UV-Vis) spectroscopy, Infrared (IR), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS) are time-consuming, labor-intensive, and prone to human error. By automating spectrum interpretation, finding patterns in massive datasets, and increasing the precision of component identification and quantification, AI-driven methods—especially those built on machine learning and deep learning—offer potent substitutes. With a focus on case studies in metabolomics, pharmaceuticals, and environmental analysis, this study examines recent developments in AI applications across a range of spectroscopic modalities.  We also go over the difficulties with data quality, model generalizability, and the requirement for explainable AI to guarantee dependability in crucial decision-making situations. It is anticipated that AI's contribution to analytical chemistry will grow as it develops further, opening up new possibilities in real-time monitoring, predictive diagnostics, and high-throughput analysis.