AI in Pharmaceutical Analysis: A Comprehensive Review

Artificial intelligence (AI) is rapidly transforming pharmaceutical analysis by shifting conventional analytical practice from labor-intensive, trial-and-error methods toward faster, data-driven, and predictive approaches. The increasing complexity of pharmaceutical formulations and the large volume of data generated by modern analytical instruments have created a strong need for intelligent computational tools capable of accurate interpretation and decision-making. In this context, AI, particularly machine learning and deep learning, has emerged as a powerful support system in pharmaceutical analysis. Its applications extend across chromatographic analysis, spectroscopic techniques, and mass spectrometry, where it improves pattern recognition, peak analysis, classification, prediction, and method optimization. AI also contributes significantly to pharmaceutical quality control, process analytical technology, and real-time monitoring by enabling automation, anomaly detection, and predictive maintenance. In addition, AI supports the principles of green analytical chemistry by helping reduce solvent consumption, optimize experimental design, and improve sustainability in analytical practices. This review presents a comprehensive overview of the role of AI in pharmaceutical analysis, highlighting its major applications, benefits, current challenges, and future prospects in advancing accuracy, efficiency, and innovation in the pharmaceutical sector.