A Review on Machine Learning Approaches for Solid Waste Management

The rapid increase in population and the ongoing expansion of urban regions have resulted in a substantial growth in municipal solid waste generation, creating serious challenges for environmental protection and urban management. In response to these problems, recent research has increasingly focused on technological solutions, among which machine learning has gained considerable attention. Machine learning can capture complex nonlinear patterns and is therefore widely applied across various stages of municipal solid waste management to enhance sustainable and efficient waste handling. This review examines over one hundred research studies published between 2000 and 2022, with the objective of analyzing how machine learning techniques have been employed throughout the waste management process, including waste generation prediction, collection scheduling, transportation optimization, and disposal planning. The study systematically explores prevailing research trends, identifies methodological limitations, and highlights promising future research directions, offering conceptual understanding and practical guidance for subsequent investigations. In contrast to previous review studies, this research specifically focuses on the waste generation and disposal stages, highlighting how individuals, households, and municipal authorities employ advanced computational techniques to minimize waste volume and improve management efficiency. The findings indicate that most existing studies focus on waste classification, regional estimation of waste quantities, and prediction of bin fill levels. Nevertheless, several important challenges remain, such as the lack of real-time time-series datasets, limited model robustness and generalization capability, the absence of unified benchmarking standards, and the difficulty of achieving reliable long-term forecasting of waste generation.

@article{Vidya2026,
  title     = {A Review on Machine Learning Approaches for Solid Waste Management},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {S. Vidya},
  doi       = {10.14569/IJACSA.2026.0170314},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170314}
}