Green Chemistry, Circular Economy, and Artificial Intelligence: Towards Sustainable and Regenerative Industrial Systems in Algeria

Green chemistry, formalized in the mid-1990s, provides a design framework for reducing or eliminating hazardous substances, minimizing waste, and improving the overall sustainability of chemical products and processes. In parallel, the circular economy has emerged as a systems-level paradigm that seeks to retain the value of materials, components, and products for as long as possible through reuse, repair, remanufacturing, recycling, and regeneration. More recently, artificial intelligence (AI) has become a major enabling layer for both domains by supporting molecular discovery, reaction prediction, process optimization, autonomous experimentation, and the intelligent management of circular material flows. This review examines the converging roles of green chemistry, circular economy, and AI in the transition toward sustainable and regenerative industrial systems. Particular emphasis is placed on recent advances in foundation models for chemistry, self-driving laboratories, digital twins, Green AI, AI-assisted catalyst discovery, and digital product passports. We also discuss the relevance of these developments for resource-intensive and environmentally sensitive contexts, including the Algerian industrial landscape and its coastal zones, while avoiding unsupported quantitative claims. Finally, we address major challenges related to data quality, model bias, reproducibility, scalability, governance, and the carbon footprint of AI. Taken together, the evidence suggests that the convergence of these three fields can accelerate safer chemistry, circular manufacturing, and industrial decarbonization, provided that technological innovation is coupled with robust environmental assessment and responsible implementation.