## 3 Results
### Summary of Thematic and Conceptual Clusters  
Overall, the analyzed literature highlights a growing focus on the application of digital and intelligent solutions to address environmental challenges in waste management. AI is emerging not only as a predictive tool but also as a strategic enabler for the digitalization of material flows, real-time environmental monitoring, optimization of production cycles, and sustainable design. Emerging technological trajectories indicate a shift towards predictive and integrated models capable of reducing environmental impacts, valorizing waste as a resource, and supporting the transition to a circular economy.  
This study is characterized by its integrated methodological approach, which combines bibliometric analysis with qualitative thematic review. This synergy enabled a comprehensive understanding of current research trajectories, mapping areas of high scientific intensity and identifying interdisciplinary connections between predictive modeling tools, digital simulation, and sustainability strategies. In particular, the emphasis on the role of AI, DT, and hybrid ML models offers an original contribution aimed at promoting a systemic and data-driven perspective on waste management.  
Table 8 summarizes the results of the analysis in a structured format, associating each identified macro-theme with key authors and relevant keywords selected exclusively from those emerging in the bibliometric map (frequency \(\geq\) 3). This organization highlights the main research lines, the most conceptually dense areas, and the semantic relationships among the thematic clusters. The combined approach, integrating conceptual clusters and keyword co-occurrence analysis, has provided a coherent and structured representation of the current scientific landscape, confirming the multidimensional and interdisciplinary nature of digital applications in sustainable waste management.  
To complement the thematic clusters and enrich methodological understanding, Table 9 provides a concise overview of the main AI and ML techniques adopted in each research area. For each macro-theme, the table specifies the computational models, the underlying logic (e.g., regression, classification, and simulation), and the corresponding functional purposes (e.g., prediction, optimization, and monitoring). This summary helps clarify how digital methods concretely support scientific and operational advances in sustainable waste management.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Macro Theme** & **References** & **Associated Keywords** \\ \hline AI for forecasting and managing waste generation & [35; 45; 46; 47] & waste management, urban waste, prediction, machine learning \\ \hline AI and ML for recycled materials and sustainable concrete & [36; 37; 38; 39; 40; 42] & recycling, artificial intelligence, optimization \\ \hline Digitalization and circular economy & [43; 50] & digital twin, circular economy \\ \hline E-waste, heavy metals, and environmental management & [48; 49; 52; 53] & air pollution, human, environmental monitoring \\ \hline Deep learning and environmental monitoring & [44; 54] & deep learning, sustainable development goal, zero carbon \\ \hline Integration of AI, industrial systems, and DT & [51] & digital twin, artificial intelligence \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of constructs. Our elaboration.  
This methodological breakdown reinforces the multidimensional nature of the field, showing how specific AI and ML techniques are strategically deployed to address diverse operational and environmental challenges. The integration of regression, simulation, and classification approaches across application domains highlights a growing convergence toward intelligent, adaptive, and predictive waste management systems.