The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions

The integration of artificial intelligence (AI) plays a crucial role in modern analytical chemistry, offering solutions to long-standing challenges. Conventional techniques, such as spectrophotometric analysis and chromatography, often face issues like spectral overlap, matrix interference, and extensive experimental optimization. AI and machine learning (ML) approaches address these limitations by enabling spectral deconvolution, pattern recognition, prediction of retention factors, and automated optimization of separation conditions. Beyond enhancing traditional methods, AI supports the development of innovative analytical platforms. Modern analytical chemistry increasingly relies on smartphone- and paper-based sensors for on-site detection of biomarkers and pollutants. These portable, low-cost systems generate complex datasets requiring advanced computational tools, where AI can improve reliability and sensitivity when validated. AI also plays a vital role in synthesizing and optimizing nanomaterials such as carbon quantum dots (CQDs), accelerating experimental fine-tuning through predictive modeling and optimization algorithms. Moreover, AI facilitates the interpretation of large-scale data, providing deeper insights while reducing human error and analysis time. Despite these advancements, challenges remain regarding model interpretability and the integration of heterogeneous datasets. Addressing these requires explainable ML methods that bridge computational predictions with chemical reasoning. This review highlights current AI applications in chromatographic analysis, drug stability studies, and modern analytical chemistry, discusses implementation challenges, and explores future trends shaping the next generation of intelligent analytical systems.