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The rapid growth of global waste generation has led to an increased need for sustainable waste management (SWM) strategies. Recent studies have explored the potential of artificial intelligence (AI) techniques to improve existing SWM schemes throughout their various stages, from collection to final disposal. This systematic literature review (SLR) analyzes the use of AI models in SWM and discusses their advantages, limitations, and potential applications.The SLR identifies several AI techniques used in SWM, including individual and hybrid models such as artificial neural networks (ANN), expert systems, genetic algorithms (GA), and fuzzy logic (FL). These models have been applied to various SWM fields, including waste generation patterns, waste collection truck routes, waste container monitoring, and final disposal site location.Despite the potential benefits of AI techniques in SWM, the SLR identifies challenges and limitations such as data quality and availability, complexity of waste management systems, and lack of standardization in AI-based solutions. The review concludes by recommending further research on the development and testing of hybrid AI-based models that can better address the complexities of SWM systems. Additionally, standardization is necessary to ensure interoperability and scalability.Overall, this SLR provides a comprehensive overview of AI applications in SWM and highlights their potential to improve waste management practices. However, more research is needed to overcome the challenges and limitations identified in the review.