Role of AI in reducing global Plastics use: Predictive analytics for global sustainability

The growth, from a global perspective, in plastic waste sees its necessary promise in Artificial Intelligence. The driving force of Machine Learning, Computer Vision, and Predictive Analytics, shapes each style of waste management and effective collection routes to recycling. In this paper, therefore, the discussion will be on how AI can ensure a reduction in plastic waste, focusing on developing countries such as Nigeria. The theoretical underpinning of this research is on AI adoption, the Technology Acceptance Model, a few real-world case studies in AI for waste reduction, and many challenges that need to be focused on due to issues of data sparsity, infrastructure limitations, and ethics. Using these challenges for unlocking the full potential of AI in the direction of a more sustainable future, with minimal plastic waste at the forefront of environmental well-being, could be better negotiated. As the global community faces the pressing need to tackle plastic pollution, especially in areas with inadequate waste management systems and severe environmental challenges AI technologies present groundbreaking solutions to improve waste management methods, eliminate environmental harm, and foster sustainable growth. A crucial factor influencing the future of AI-driven plastic waste reduction in developing countries is the ongoing progress and implementation of AI technologies.