This study presents a comprehensive review of the emerging role of Generative Artificial Intelligence (GenAI) in environmental assessment and sustainability analysis. Positioned within a new paradigm of environmental management, GenAI redefines traditional static models through dynamic, generative, and participatory approaches that integrate data synthesis, scenario modeling, and governance insight. Using a Systematic Literature Review (SLR) guided by the CIMO (Context–Intervention–Mechanism–Outcome) framework, this paper identifies and analyzes 182 scholarly and technical publications published between 2015 and 2025. The review synthesizes developments across key GenAI architectures—Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based Large Language Models (LLMs), and Diffusion Models—and evaluates their applications in synthetic data generation, scenario simulation, remote sensing, predictive analytics, and public engagement. The findings reveal that GenAI holds significant potential to address data scarcity, enhance model scalability, and promote participatory and interdisciplinary decision-making, while also presenting challenges related to interpretability, data bias, validation, environmental footprint, and ethical governance. To guide responsible implementation, the study proposes a three-tier framework emphasizing technical fidelity, transparency, and governance alignment. The implications underscore that effective integration of GenAI into environmental management requires hybrid modeling, participatory data governance, and sustainable AI infrastructures to ensure transparency, accountability, and equity. Collectively, this work advances an evidence-based understanding of how GenAI can underpin a data-driven, inclusive, and ethically responsible paradigm in environmental assessment.
