Artificial Intelligence-Based Waste Management: A Review of Classification, Techniques, Issues, and Challenges

Artificial intelligence (AI) is emerging as a transforming force in waste management practices, enabling new ways of bringing efficiency and effectiveness. This survey presents methods related to waste management, which are categorized systematically for understanding the effectiveness of various AI-based techniques. The study undertakes a critical review of relevant research works that epitomize major advances and methodologies of AI-driven waste management. The manuscript provides an exhaustive taxonomy, dividing AI methods into Supervised Learning, Unsupervised Learning, and Reinforcement Learning, and then subdividing Supervised Learning into four broad categories: Machine Learning-based Classification, CNNs, Transfer Learning, and Hybrid or Ensemble Learning. We further evaluate different datasets applied in performance benchmarking and the efficacy of the various AI models. We also discuss some critical issues, such as the problem of available data quality, poor generalization of models, and integration of systems. Future research directions, which would go a long way toward helping to surmount these challenges, are also discussed. This survey aims to present a structured framework for understanding current AI applications in waste management, therefore guiding ongoing and future research in the field.