Environmental communication plays a critical role in shaping public understanding, attitudes, and actions toward environmental sustainability and climate change. Environmental challenges such as climate change, deforestation, pollution, and biodiversity loss require effective communication strategies that can reach diverse audiences and inspire action. Traditionally, environmental communication has relied heavily on textual reports, policy documents, and scientific publications, which are often inaccessible to non-expert audiences. With the rapid advancement of Artificial Intelligence (AI), new possibilities have emerged for enhancing environmental communication through multimodal approaches that integrate text, image, and sound. This study explores how AI-driven multimodal systems can be leveraged to improve the effectiveness, accessibility, and persuasiveness of environmental messages. Drawing on Technology Acceptance Model (TAM) and Media Richness Theory, the study examines the role of natural language processing, computer vision, and speech technologies in environmental advocacy, education, and policy communication. This study adopts a qualitative research design, combining conceptual analysis, content analysis, and case-based review to examine Artificial Intelligence in multimodal environmental communication. By addressing existing gaps in traditional environmental communication, this research highlights the potential of AI-enabled multimodal communication to foster greater public engagement, environmental awareness, and sustainable behavioural change. The study contributes to the growing interdisciplinary discourse at the intersection of artificial intelligence, communication studies, and environmental sustainability.