This chapter explores the role of artificial intelligence (AI) as a catalyst for promoting health equity in ophthalmology and public eye care systems. AI technologies, including deep learning (DL), machine learning (ML), and natural language processing (NLP), have demonstrated remarkable capabilities in screening, diagnosis, and population-level surveillance of diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Beyond clinical diagnostics, AI supports equitable healthcare delivery through predictive modeling, targeted health communication, and intelligent resource allocation. When applied responsibly, these systems can expand access to underserved communities, streamline teleophthalmology services, and strengthen population-based vision screening programs. However, the chapter also highlights critical challenges, including algorithmic bias, lack of data diversity, privacy risks, and infrastructural limitations in low-resource settings. To achieve equitable AI deployment, the chapter emphasizes ethical governance, data solidarity, stakeholder collaboration, and alignment with global frameworks such as the WHO’s principles for trustworthy AI and the EU Artificial Intelligence Act. Achieving fairness in AI-enabled ophthalmology requires inclusive datasets, transparent decision-making, workforce readiness, and policies that ensure accountability. Ultimately, AI can serve as a transformative instrument for reducing global disparities in vision care—if guided by equity, ethics, and sustainability.