This chapter examines the intersection of artificial intelligence, sustainable development goals, and communication for development in an era of profound technological change. As AI reshapes the global economy, it presents both opportunities and challenges for achieving the Sustainable Development Goals (SDGs) by 2030. The chapter traces the evolution of AI from its theoretical foundations in Turing’s work to contemporary generative AI applications, exploring how this technology is being integrated into national development strategies worldwide. The chapter applies both theoretical and practical approaches to AI utilization. The analysis reveals a significant disparity in AI infrastructure and capability between developed and developing nations, with investment and innovation concentrated in high income countries. This digital divide threatens to exacerbate existing inequalities, potentially undermining the SDGs’ core principle of “leaving no one behind.” The chapter critically examines the absence of a dedicated communication framework within the SDGs and explores how AI tools might be leveraged to communicate development goals more effectively across diverse contexts. Central to this work is the introduction of the AI Sustainability Exclusion Theory (ASET), which conceptualizes five interconnected forms of bias that systematically exclude populations from AI’s benefits: algorithmic bias, consumption bias, inequality bias, opportunity bias, and linguistic and cultural bias. Drawing on empirical evidence and scholarly literature, the theory demonstrates how these biases operate across multiple dimensions from healthcare and employment to energy access and cultural representation, creating new mechanisms of exclusion that threaten sustainable development progress. The chapter concludes that while AI offers powerful potential for communicating and advancing the SDGs, realizing this potential requires conscious effort to address exclusionary biases through participatory co-creation, multi-stakeholder governance, diversified datasets, strategic infrastructure investments, and continuous fairness monitoring. Only through such comprehensive approaches can AI become a force for inclusive development rather than a new vector for perpetuating global inequality in the crucial years leading to 2030 and beyond.