## 5 Conclusions  
The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) represents a paradigm shift in the optimization of reverse supply chains of solid waste (RSCSW) within the circular economy framework. Compared to traditional practices, this integration delivers substantial improvements in operational efficiency, cost reduction, and material recovery. Furthermore, it enhances governance and sustainability by promoting transparency, traceability, and environmental stewardship. However, significant challenges remain, including regulatory fragmentation, technological barriers, and the need for stakeholder collaboration. Future research should focus on developing comprehensive frameworks that combine MCDA, AI, and emerging technologies like blockchain and IoT to further enhance the efficiency and sustainability of RSCSW. Policymakers and industry leaders must also prioritize the development of cohesive regulatory frameworks and invest in technological infrastructure to support the widespread adoption of these advanced decision-making tools. In terms of the theoretical contribution and framework differentiation, the proposed framework advances existing theory by integrating MCDA and AI into a holistic governance model for RSCSW. Unlike traditional approaches, which often focus on isolated aspects of waste management, this framework offers a systemic view that balances economic, environmental, and social criteria [17]. Moreover, the incorporation of emerging technologies, such as blockchain and IoT, fosters greater transparency and operational efficiency, aligning with the principles of the circular economy [14]. This innovative approach not only optimizes waste management but also contributes to the creation of more resilient and sustainable supply chains. Although this study demonstrates significant advancements in RSCSW governance through the integration of MCDA and AI, several limitations must be acknowledged to ensure a balanced interpretation of the findings: (1) The reviewed literature may be subject to publication bias, as studies with positive results are more likely to achieve academic dissemination [40]. This could lead to an overestimation of the benefits of integrating MCDA and AI in waste management. To mitigate this bias, future reviews should consider including gray literature and unpublished studies. (2) The heterogeneity of the methods employed in the analyzed articles hinders direct comparisons between different approaches [41]. This heterogeneity can be attributed to regional, sectoral, and methodological differences. For instance, studies conducted in developed countries often benefit from advanced technological infrastructure, while those in developing countriesface challenges such as limited resources and inadequate infrastructure. Additionally, the diversity of sectors addressed (e.g., plastic waste, electronic waste, organic waste) and the variety of MCDA and AI techniques used contribute to the variability of the results. (3) The certainty of the evidence presented in this review should be interpreted with caution, as many of the included studies have methodological limitations, such as small sample sizes, lack of randomization, and reliance on case studies from specific regions. These limitations may affect the generalizability of the findings, particularly in contexts with limited technological infrastructure [35]. In terms of challenges, the implementation of AI for governance faces significant technical and operational challenges, such as the need for advanced computational infrastructure and high-quality data. These challenges may limit the applicability of MCDA and AI in emerging countries, where such resources are often scarce. These limitations highlight the need for future research focused on the empirical validation of these models in different socio-economic contexts, as well as the development of standardized frameworks to address heterogeneity and improve the robustness of findings.