<p>The combination of artificial intelligence, data science and green chemistry is leading scientific activity in addressing the planetary scale environmental challenges. This collective approach is to minimise waste and maximise energy efficiency, in addition to safer chemical design. Green chemistry is pivotal for developing more sustainable processes and production systems that minimize harm to human health and the environment. The integration of artificial intelligence and data science is transforming green chemistry. These tools can now analyze very large and complex datasets, predict chemical behaviour and help optimize processes to make them more sustainable. This review highlights recent advances in artificial intelligence for reaction optimization, new material discovery, process automation, and real-time environmental monitoring. It shows how machine learning, cheminformatics and data analytics work together to accelerate innovation in green and sustainable chemistry. Although this transformation is promising, major challenges still exist, such as the lack of high-quality data, the difficulty in interpreting AI models, and the limited collaboration across disciplines. To overcome these barriers, the development of data standards, open platforms, and interconnected research ecosystems is needed. By unlocking the potential of artificial intelligence and data science for green chemistry, this work highlights a strategic route to sustainable development goals achievement and to a cleaner more sustainable future.</p>