AI Driven Green Analytical Chemistry in Pharmaceutical Technology

Abstract

Pharmaceutical laboratories depend on analytical procedures that are selective, accurate, robust, and suitable for regulated decisions, yet conventional method development can consume substantial solvent, energy, sample, and analyst time. Green analytical chemistry (GAC) seeks to reduce these burdens without weakening the quality of analytical information. Machine learning (ML), chemometrics, and automated optimization can support this goal by predicting analytical responses, selecting informative experiments, and optimizing competing objectives. This critical narrative review examines how artificial intelligence (AI) and ML can be integrated with GAC and analytical quality by design (AQbD) for pharmaceutical method development. It distinguishes established design-of-experiments and chemometric workflows from newer data-driven and closed-loop AI approaches, reviews their use in chromatography and spectroscopy, and evaluates solvent hazard, waste, energy, preparation, and lifecycle metrics. Published examples include neural-network-assisted reversed-phase HPLC, Bayesian optimization of green supercritical fluid chromatography, automated feedback-controlled liquid chromatography, and recent AI-predicted HPLC methods. The evidence indicates that AI can reduce experiment burden and expose useful trade-offs, but it does not by itself ensure a greener or more reliable method. Small and heterogeneous datasets, narrow optimization targets, incomplete environmental inventories, limited external validation, and poor reporting constrain generalization. The review proposes a human-supervised workflow that embeds environmental objectives in the analytical target profile, uses uncertainty-aware experiment selection, validates the final procedure under current ICH guidance, and reports analytical performance and sustainability transparently. Priorities include shared datasets, interpretable models, prospective comparisons, and lifecycle-aware optimization.