Environmental issues have continued to spur discussions, debates, public outrages, and awareness campaigns, inciting interest in emerging technologies such as Artificial Intelligence. Its usage is spread across many environmental industries, including wildlife protection, natural resource conservation, clean energy, agriculture, energy management, pollution control, and waste management. In 2017, at the United Nations Artificial Intelligence Summit in Geneva, the UN acknowledged that AI could be an enabler in the sustainable development process towards peace, prosperity, and dignified life for humankind and proposed to refocus on the application of AI in assisting global efforts on sustainable development to eradicate poverty, hunger and to protect the environment as well as to conserve natural resources. It is vital to address environmental sustainability concerns; however, with the advent of AI, most common environmental issues are now solvable by prioritizing human interests. Sustainability encompasses the interrelated areas of the environment, society, and economy. According to the United Nations’ “Our Common Future,” also known as the “Brundtland Report,” it is defined as “development that satisfies current needs without compromising the ability of future generations to meet their own needs.” Unfortunately, the Earth is currently facing serious consequences from global warming and climate change, and immediate action is required to encourage the use of environmentally friendly and sustainable products to address these issues. Environmental degradation and climate change are numerous environmental concerns requiring novel and intelligent artificial intelligence solutions. The literature on AI and environmental sustainability encompasses various domains. Notably, AI is being used to address the bulk of regional and global environmental concerns, including energy, water, biodiversity, and transportation, even though many of these sectors have permeated and evolved. However, there is a need to combine current literature on the application of AI, particularly in relation to environmental sustainability in areas such as energy, water, biodiversity, and transportation. There is a significant lack of research on how AI can promote environmental sustainability. This research aims to explore how AI can be applied to address environmental issues in various sectors to achieve the Sustainable Development Goals (SDGs).

This article examines the potential for artificial intelligence (AI), blockchain, and the Internet of Things (IoT) to advance sustainability. Through a literature review and critical analysis, the study evaluates the possible advantages, difficulties, and opportunities of utilizing these technologies to support a sustainable future. The research study emphasizes how effective AI is at streamlining resource management, increasing system efficiency, and optimizing energy use. It focuses on the potential of blockchain to improve supply chain accountability and transparency, and it also discusses the game-changing potential of IoT to improve resource management. However, some issues must be resolved, including excessive costs, technological difficulties, data privacy concerns, and social repercussions. The essay advocates creating multidisciplinary research programs, funding R&D, and supporting collaborative relationships. It also suggests creating sustainable implementation plans, prioritizing ethical issues and data governance, and encouraging information exchange and awareness. By accepting these proposals, stakeholders may leverage the promise of green technology and innovation to build a sustainable future. It is also clear that the Internet of Things (IoT) can potentially optimize resource management. Real-time data on a variety of topics, including traffic conditions, air and water quality, and water management, can be provided through IoT-enabled sensors. Cities may reduce traffic, increase energy efficiency, enhance environmental conditions, and encourage sustainable water management techniques by utilizing this data to inform their decisions. However, serious consideration must be given to data privacy, security, scalability, and interoperability issues to ensure IoT solutions’ ethical and efficient adoption. Despite their enormous potential, the paper acknowledges the difficulties and constraints in implementing these technologies. Significant obstacles include high implementation costs, complex technical requirements, and the requirement for adequate data privacy and security safeguards. A sustainable and inclusive future also requires resolving ethical issues, including algorithmic prejudice, social fairness, and equitable access to technology. The report recommends encouraging cooperative relationships between academia, business, government, and communities to address these issues. Research and development investments are required to evaluate these technologies’ practical use, scalability, and economic viability. In addition, multidisciplinary research initiatives can comprehensively comprehend green technology and innovation’s social, economic, and environmental effects. It has been concluded that there is great potential for future technologies, such as AI, blockchain, and IoT, to advance sustainability. Stakeholders can use these technologies’ revolutionary potential to build a sustainable future by resolving obstacles, promoting collaboration, and doing additional research. To ensure the ethical and successful application of green technology and innovation for the benefit of the environment and future generations, it is essential to prioritize ethical considerations, establish sustainable implementation strategies, and foster information exchange and awareness.

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.

Water quality prediction plays an important role in environmental monitoring and ecosystem sustainability in the Amazon. Therefore, this review focuses on determining the advances in the scientific production of artificial intelligence in water quality prediction in the Amazon, as well as the limitations and perspectives compared to water quality indexes (WQI). In this sense, Boolean operators were applied, using the following terms: “artificial intelligence”, “machine learning”, “water quality,” and “Amazonia” The databases were Scopus, web of Science, Springer, and IEEE. In this study, 14 scientific articles published during the period 2000-2024 focused on Amazonian countries were evaluated. Although in the Amazon low scientific production was evidenced and is led by Brazil, the highest scientific growth was for 2021, and 93% belongs to the Scopus database, with a compound annual rate of 12.16%. The IA is characterized by using data from governmental institutions and is only limited to parameters such as Total Suspended Solids (TSS), Total Organic Carbon, Turbidity, and Chlorophyll, using satellite imaging techniques, and the most commonly used algorithm was the Clustering Algorithms. In this context, AI applications are still very low in Amazonian countries compared to other European countries. Its limitations are in the accuracy and the limited amount of physicochemical and microbiological data used for predictions. However, AI is a tool that will replace the water quality indexes used manually.