Artificial Intelligence and Machine Learning Applications in Pharmaceutical Analysis: From Traditional Analytical Approaches to Intelligent Pharmaceutical Systems – A Comprehensive Review

Pharmaceutical analysis underpins the identity, purity, potency and safety of medicines. Conventional workflows based on chromatography, spectroscopy and mass spectrometry are reliable but depend on manual interpretation and generate data volumes that are hard to exploit fully. Artificial intelligence (AI) and machine learning (ML) offer data-driven tools for peak detection, retention-time and spectral prediction, impurity identification, dissolution and stability modelling, anomaly detection and real-time process analytical technology (PAT). This narrative review, based on a structured search of peer-reviewed and regulatory sources published mainly from 2021 to 2026, compares traditional and AI-enabled workflows, summarises ML methods and their applications across analytical areas, and examines explainable AI, digital twins, generative AI and autonomous laboratories. In the sources reviewed, the strongest evidence concerns spectroscopy-based prediction for PAT and dissolution and retention-time prediction supported by large public datasets, whereas evidence for stability forecasting, rare-impurity identification and autonomous laboratories is limited and seldom validated at industrial scale. Regulatory thinking is converging on risk-based, context-of-use credibility assessment and lifecycle management. We propose a conceptual Intelligent Pharmaceutical Analysis Framework (IPAF) linking instrumentation, data integrity, AI analytics, decision support and governance, and identify priorities: standardised validated datasets, explainable and uncertainty-aware models, and prospective multi-site validation.