## 5 Concluding Remarks and Future Perspectives  
The literature review has highlighted the significant potential of artificial intelligence (AI) tools for predicting and optimizing environmental control and remediation processes, particularly in adsorption-based systems. Neural networks (NNs) have shown clear advantages over traditional modeling programs due to their self-adjusting capabilities, generalization, and high precision. However, several challenges remain that must be addressed to improve the practical application of these tools. One key limitation is the need for extensive validation, which can be time-consuming, especially when dealing with large datasets. For instance, neural network models trained on small datasets may not achieve the desired accuracy while processing large datasets can lead to computational challenges. Additionally, the inability of NNs to trace relationships between dependent and independent variables complicates the interpretation of results, limiting their usefulness in understanding complex environmental systems. To overcome these obstacles, reliable and well-curated datasets are essential. Researchers must prioritize data sharing and accessibility to enhance the accuracy and reproducibility of NN-based models globally. Simple NNs still exhibit low reproducibility when applied to large-scale or global systems, which has led to a growing trend towards hybrid models. These models offer greater flexibility and enhanced analytical capabilities, combining the strengths of traditional machine learning approaches with the adaptability of neural networks. Looking forward, future developments in this field will likely center around hybrid AI models and their integration with conventional networks. Hybrid systems provide better performance, addressing limitations in standard neural networks by improving their adaptability to complex environmental scenarios. The incorporation of real-time data through the IoT (Internet of Things) and sensor networks could further enhance the accuracy and responsiveness of predictive models in environmental management. The rise of explainable AI (XAI) also presents a promising future avenue. XAI aims to address the \"black box\" nature of NNs by making the decision-making process of these models more transparent and interpretable, thereby fostering greater trust and adoption in regulatory and decision-making frameworks. Moreover, AI applications are expected to reduce global operating costs in water treatment facilities and other environmental remediation processes. As AI tools become more efficient and accessible, industries may shift away from energy-intensive traditional methods, leading to both economic and environmental benefits. In conclusion, while there are still challenges in fully realizing the potential of AI in environmental remediation, the combination of improved data management, advanced hybrid models, and integration with real-time technologies will undoubtedly play a critical role in advancing the field. With these developments, AI can be a transformative tool in addressing global environmental challenges. Conceptualization, G.M.M.S. ; methodology, M.F.P.d.C. and A.R.S. ; software, M.F.P.d.C. ; validation, G.M.M.S., R.d.S.A. and L.P.; formal analysis, M.F.P.d.C. ; investigation, M.F.P.d.C. ; resources, G.M.M.S. ; data curation, M.F.P.d.C. and A.R.S. ; writing--original draft preparation, M.F.P.d.C. ; writing--review and editing, G.M.M.S., R.d.S.A., A.R.S. and L.P.; visualization, G.M.M.S. ; supervision, G.M.M.S. ; project administration, G.M.M.S. ; funding acquisition, G.M.M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments:** We acknowledge the Federal Institute of Ceara, Brazil, for the authorization to carry out the post-doctorate study.