Artificial Intelligence-Driven Advances in Analytical Chemistry: Methods, Applications, and Future Perspectives

Abstract

Abstract Artificial intelligence (AI) is reshaping analytical chemistry by changing how instrumental measurements are processed, interpreted, optimized, and translated into chemical decisions. Modern analytical platforms generate high-dimensional spectra, chromatograms, mass spectra, images, and sensor signals in volumes that can exceed the practical capacity of conventional univariate or manually supervised workflows. This integrative review examines AI-driven advances across spectroscopy, chromatography, mass spectrometry, nuclear magnetic resonance (NMR), chemical sensors, imaging, and analytical method development. The review adopts a structured narrative design using purposive sampling of peer-reviewed reviews, primary methodological literature, and regulatory guidance identified through targeted searches of major biomedical and chemistry literature sources and official regulatory repositories. Evidence was thematically coded into four domains: data enhancement, predictive modelling, analytical-method optimization, and decision support. The synthesis indicates that machine learning (ML), deep learning (DL), chemometrics, quantitative structure–retention relationships (QSRR), and hybrid mechanistic–data-driven approaches can improve pattern recognition, spectral deconvolution, analyte identification, retention prediction, calibration, anomaly detection, and experimental optimization when adequate and representative data are available. However, AI does not automatically create a valid analytical method: model leakage, biased sampling, poor external validation, instrument drift, explainability limitations, and weak uncertainty characterization can undermine apparent performance. Current regulatory thinking increasingly emphasizes risk-based credibility and conventional analytical validation principles. Future analytical laboratories are therefore likely to combine AI with robust metrology, automated experimentation, digital twins, explainable AI, multimodal data fusion, and continuous lifecycle validation. The central conclusion is that the strongest pathway is not replacement of analytical chemistry by AI, but integration of chemical knowledge, measurement science, and trustworthy computation.