Abstract
<jats:p>The transition toward environmentally sustainable pharmaceutical manufacturing requires standardized methodologies that integrate digital technologies with established sustainability assessment tools. Although artificial intelligence (AI), life cycle assessment (LCA), Green Chemistry metrics and Digital Twin (DT) technologies have individually demonstrated considerable potential for improving manufacturing sustainability, their implementation remains fragmented, limiting reproducibility and routine application in industrial and academic settings. Here we present the Artificial Intelligence-Integrated Circular Solvent Sustainability Assessment (AI2CS2A) Protocol, a standardized laboratory-to-decision methodology that integrates experimental process data acquisition, Green Chemistry metrics, LCA, explainable artificial intelligence (XAI), DT simulation, circular solvent management and environmental decision reporting within a unified Environmental AI workflow. The protocol begins with systematic acquisition and validation of manufacturing process data, followed by calculation of Process Mass Intensity (PMI), Circular Process Mass Intensity (cPMI), and complementary Green Chemistry metrics. Environmental impacts are quantified using LCA, including Global Warming Potential (GWP) and Cumulative Energy Demand (CED). A standardized Random Forest workflow is then used for AI-assisted solvent sustainability evaluation, while XAI provides transparent interpretation of model predictions. DT simulation is subsequently used to evaluate solvent recovery strategies, material circulation and alternative operating scenarios before physical implementation. These outputs are integrated through the Overall AI2CS2A Sustainability Index (OASI), a predefined composite indicator calculated from normalized process-mass intensity, solvent recovery, product yield, process energy demand and VOC emissions to provide comparative sustainability benchmarking within a defined protocol execution. OASI is not a machinelearning prediction or a measure of AI model accuracy; rather, it provides a reproducible comparative summary of selected process-level sustainability indicators. OASI is not a machine-learning prediction or a measure of AI model accuracy; rather, it provides a reproducible comparative summary of selected process-level sustainability indicators. The workflow culminates in validated, protocol-derived analytical outputs that are integrated into the AI2CS2A Dashboard v2.0 and the Integrated Environmental Decision Report (IEDR), which consolidates analytical results, AI validation, DT scenarios, sustainability indicators, uncertainty analyses and evidence-based environmental recommendations into a traceable decision-support document. Representative outputs are illustrated using a literature-derived manufacturing process for the active pharmaceutical ingredient (API) Sertraline. Although demonstrated using pharmaceutical manufacturing, the AI2CS2A Protocol is broadly applicable to solvent-intensive chemical industries seeking standardized, transparent and reproducible approaches to environmental sustainability assessment, circular resource management, industrial decarbonization and climate-resilient manufacturing.</jats:p>