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.

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.

The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, real-time processing demands, and resilience in dynamic environments. Key safety mechanisms—such as collision avoidance, anomaly detection, emergency coordination, and fail-safe operations—are analyzed to demonstrate how AI agents contribute to operational reliability. The study also explores regulatory compliance, focusing on emission control, real-time monitoring, and data governance. Implementation challenges, including limited onboard computational power, legal and ethical constraints, and interoperability issues, are addressed with practical solutions such as edge AI and modular architectures. Finally, the article outlines future research directions involving smart port integration, scalable AI models, and emerging technologies like federated and explainable AI. This work highlights the transformative potential of AI agents in advancing autonomous maritime transportation.

The maritime industry is undergoing a digital transformation, integrating automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and safety. However, this technological evolution has also increased cybersecurity vulnerabilities, exposing vessels, ports, and maritime communication networks to sophisticated cyber threats. This systematic review, conducted following the PRISMA guidelines, examines the current landscape of AI-driven cybersecurity solutions in maritime environments. By analyzing peer-reviewed studies and industry reports, this review identifies key AI methodologies, including machine-learning-based intrusion detection systems, anomaly detection mechanisms, predictive threat modeling, and AI-enhanced zero-trust architectures. This study assesses the effectiveness of these techniques in mitigating cyber risks, explores their implementation challenges, and highlights existing research gaps. The findings indicate that AI-powered solutions significantly enhance real-time threat detection and response capabilities in maritime networks, yet issues such as data scarcity, regulatory constraints, and adversarial attacks on AI models remain unresolved. Future research directions should focus on integrating AI with blockchain, federated learning, and quantum cryptographic techniques to strengthen maritime cybersecurity frameworks.

Maritime operations play a critical role in global trade but face persistent safety challenges due to human error, environmental factors, and operational complexities. This review explores the transformative potential of Large Language Models (LLMs) in enhancing maritime safety through improved communication, decision-making, and compliance. Specific applications include multilingual communication for international crews, automated reporting, interactive training, and real-time risk assessment. While LLMs offer innovative solutions, challenges such as data privacy, integration, and ethical considerations must be addressed. This review concludes with actionable recommendations and insights for leveraging LLMs to build safer and more resilient maritime systems.

This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML’s potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems.