Abstract

ARTIFICIAL INTELLIGENCE IN ADVANCED METHOD PHARMACEUTICAL ANALYSIS: FROM MACHINE LEARNING TO INTELLIGENT DRUG QUALITY ASSESSMENT

Amartya Mitra, Dr. Ragni Kumari^*

Abstract

Background: Pharmaceutical analytical science is experiencing a transformative shift with the integration of artificial intelligence (AI) and machine learning (ML) into advanced analytical platforms. Conventional analytical methods often require extensive manual interpretation, prolonged analysis time, and expert intervention. AI-assisted analytical workflows provide opportunities for rapid data processing, intelligent pattern recognition, predictive modeling, and automated decision-making, thereby improving analytical efficiency and reliability. Objective: This review aims to comprehensively summarize recent developments in AI-assisted pharmaceutical analysis by highlighting the integration of machine learning algorithms with spectroscopic, chromatographic, and mass spectrometric techniques for smart drug quality assessment. It also discusses applications in formulation development, impurity profiling, counterfeit drug detection, process analytical technology, and regulatory perspectives. Methods: Recent literature published between 2021 and 2026 was critically evaluated from peer-reviewed scientific databases. The review covers artificial intelligence algorithms, deep learning architectures, chemo metric techniques, spectroscopy, chromatography, mass spectrometry, sensor technologies, regulatory considerations, and future research trends. Results: Machine learning and deep learning significantly enhance pharmaceutical analytical performance through automated spectral interpretation, chromatographic peak deconvolution, metabolomic profiling, impurity prediction, real-time quality monitoring, and intelligent process control. Integration with Process Analytical Technology (PAT), Internet of Things (IoT), cloud computing, and digital twins is accelerating pharmaceutical digitalization. Conclusion: Artificial intelligence represents a paradigm shift in pharmaceutical analysis by enabling intelligent, automated, and predictive quality assessment. Continued advances in explainable AI, federated learning, multimodal analytical platforms, and regulatory harmonization are expected to further transform pharmaceutical quality control and precision manufacturing.

Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Pharmaceutical Analysis; Spectroscopy; Chromatography; Mass Spectrometry; Chemometrics; Drug Quality Assessment; Process Analytical Technology.