The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles.

The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.

The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI’s role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI’s potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI’s potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness.

The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables is accompanied by new challenges, including the growing demand for rare-earth elements, the need for recycling end-of-life equipment, and the rising energy footprint of digital tools—particularly artificial intelligence (AI) models. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications in renewable energy systems. We examined empirical and conceptual studies published between 2010 and 2024 that address the deployment of AI in renewable energy, the circular economy paradigm, and model distillation and low-energy AI techniques. Our findings indicate that adopting distilled AI models can significantly reduce energy consumption in data processing, enhance grid optimization, and support sustainable resource management across the lifecycle of renewable energy infrastructures. This review concludes by highlighting the opportunities and challenges for policymakers, researchers, and industry stakeholders aiming to integrate circular economy principles into RE strategies, emphasizing the urgent need for collaborative solutions and incentivized policies that encourage low-footprint AI innovation.