## 3 Results
### Mapping Key Research Streams Through the Most Cited Contributions
Similarly, [PERSON] and [PERSON], 2016 [45] adopted a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and k-Nearest Neighbors (k-NN) to produce reliable monthly forecasts, demonstrating the applicability of such approaches to support urban decision-making. At the regional level, [PERSON] et al., 2018 [46] employed multi-layer neural networks to model waste generation and recovery in Canada, while [PERSON] et al., 2022 [47] showcased how Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), combined with cross-validation techniques, significantly improve waste disposal prediction accuracy. A second major research area focuses on the use of AI and ML for optimizing recycled materials, particularly in the construction sector. [PERSON] et al., 2021 [36] and [PERSON] et al., 2022 [39] developed predictive models to determine the mechanical and thermo-mechanical properties of recycled concrete, including rubber-modified variations. Similarly, [PERSON] et al., 2022 [40] and [PERSON] et al., 2020 [42] applied advanced techniques such as Multivariate Adaptive Regression Splines (MARS), M5 Model Tree (M5 Tree), and Least Squares Support Vector Regression (LSSVR) to evaluate compressive strength. At the same time, [PERSON] and [PERSON], 2023 [37] and [PERSON] et al., 2021 [38] used hybrid models based on Artificial Neural Networks (ANN) and nature-inspired optimization algorithms to simulate aspects such as carbonation and environmental durability of concrete, proposing data-driven solutions for sustainable building. Digitalization for circular economy purposes emerged as another transversal research theme. [PERSON], 2016 [43] proposed an integrated model to digitalize metallurgical processes and material life cycles, combining technologies such as DT and metallurgical Internet of Things (m-IoT). Likewise, [PERSON] et al., 2020 [50] illustrated the effectiveness of multi-objective and hybrid intelligence models in guiding the design of high-performance recycled concrete, contributing to more efficient and sustainable resource management. The environmental management of electronic waste and heavy metal contamination also emerged as a significant area. [PERSON] et al., 2022 [49] and [PERSON] et al., 2021 [52] applied random forest models and geospatial analysis to identify hotspots and risk sources, while [PERSON], 2021 [48], in a comprehensive review, explored the potential of ML models to simulate contamination in soil and water resources, highlighting new perspectives for environmental monitoring. In the geotechnical domain, [PERSON] et al., 2021 [53] developed AI models such as ANN, ANFIS, and Gene Expression Programming (GEP) to predict the swell strength of expansive soils, supporting sustainable infrastructure design. The role of deep learning in environmental monitoring has also been explored in the literature. [PERSON] et al., 2023 [44] analyzed the use of AI to achieve Sustainable Development Goals (SDGs), highlighting applications ranging from waste management to environmental health. Furthermore, [PERSON] et al., 2020 [54] proposed a Convolutional Neural Network (CNN)-based architecture for intelligent detection of water pollution using Near-Infrared(NIR) analysis, offering a promising solution for sustainable agriculture and water quality management. Finally, growing attention has been paid to the integration of AI, DT, and industrial systems. [PERSON] et al., 2020 [51] presented a framework that combines data-driven methodologies with the Cross-Industry Standard Process for Data Mining (CRISP-DM) to develop predictive models in production processes, with a particular focus on waste valorization and decision support in industrial contexts. To complement the thematic categorization, a temporal analysis of the most-cited contributions from 2016 to 2023 was conducted to capture the dynamic evolution of research in this field. This dimension addresses the reviewer's concern by highlighting how scholarly attention has shifted over time in response to technological advances and policy priorities.