# Sustainable Environmental Technologies: Recent Development, Opportunities, and Key Challenges
## 4 AI and ML Application in Environmental Sustainability  
Artificial intelligence (AI) and machine learning (ML) models are increasingly playing a pivotal role in addressing key challenges related to the management of natural resources, improving the accuracy of contaminant detection and optimizing remediation strategies [31; 32; 33]. AI is also being used for the timely monitoring of natural resources, including water, soil, energy, minerals, coal, oil, natural gas, biodiversity, and forests, as well as for decision assistance, in order to increase environmental sustainability. [PERSON] et al. [31] demonstrate an in-depth review on the use of AI and ML approaches and big data in the management of natural resources. This study explained the data-driven methods for decision-making, emphasizing how AI and ML can help policymakers assess the current status of natural resources, create evidence-based policies, and identify areas that require improvement. By utilizing these technologies, both public and private sectors can better manage natural resources, ensuring their responsible extraction and long-term preservation for future generations. In another study, [PERSON] et al. [32] examined the nexus between natural resources and environmental sustainability for the period 1990-2020 in 19 Asian countries through the ML approach. This study mainly focused on the impacts of the Industrial Revolution, and digitalization revealed interesting results. This research indicated that when natural resource use is between 0.1 and 9.87, CO\({}_{2}\) emission decreases, but when the range is 0-0.1 or above 9.87, the impact on CO\({}_{2}\) emission is positive. The analysis further revealed that urbanization, energy use, and economic complexity make the greatest contributions in Asian CO\({}_{2}\) predictions. This finding suggested that governments in Asian countries should use these AI/ML techniques to effectively manage their resources. Similarly, AI has demonstrated effective use for the discovery of mineral resources [33], as well as the management of water bodies, energy, transportation, and biodiversity [34]. Similarly, [PERSON] et al. [35] used a multi-indicator weighted robustness analysis to monitor planktonic communities in marine plankton ecosystems. This network technology exposed unseen topological flaws and provided a new direction for future research. The findings of this work can help with marine conservation and the sustainable management of these sources. Additionally, AI approaches are revolutionizing agriculture for sustainable and precision farming. These methods make food production more environmentally friendly and sustainable by utilizing resources more wisely. Overall, these studies advance scientific knowledge and innovative ideas that could aid in highlighting the importance and dynamic nature of sustainable environmental technology, which is not just transforming the environment and industries, but also laying the foundation for a more sustainable and eco-friendly future. The continued advancement and widespread use of these technologies will be essential to solving contemporary environmental problems on a worldwide scale.