ABSTRACT Artificial intelligence (AI) is rapidly advancing analytical technologies in chemical engineering by enabling data‐driven interpretation, automated workflows, and real‐time process decision‐making. The growing use of high‐throughput platforms, including liquid chromatography (LC)–MS, NMR, Raman, and FTIR spectroscopy, chromatography, electrochemical systems, and microfluidic devices, demands intelligent data‐processing frameworks. Machine learning, deep learning, and generative AI address challenges, including spectral deconvolution, peak resolution, matrix interference suppression, retention‐time prediction, and multicomponent quantification. This review examines AI‐enabled analytical technologies relevant to chemical engineering applications, emphasizing mechanistic insights, performance enhancement, instrument integration, and scalability. Challenges in reproducibility, interpretability, and validation are discussed, along with prospects for autonomous and self‐optimizing analytical systems.
Rayen et al. (2026) studied this question.