This chapter explores how artificial intelligence (AI) is revolutionizing clinical decision-making in ophthalmology by enhancing Clinical Decision Support Systems (CDSS). By integrating machine learning (ML), deep learning (DL), and natural language processing (NLP), AI-CDSS can analyze vast, multimodal datasets, including imaging, clinical notes, and electronic health records (EHRs), to generate precise, evidence-based recommendations at the point of care. AI-driven models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate automated diagnosis of retinal pathologies, risk stratification for glaucoma and diabetic retinopathy, and optimization of treatment plans. Additionally, NLP algorithms improve data structuring, documentation, and workflow efficiency. Despite significant progress, implementation barriers remain, including data bias, regulatory compliance challenges, interoperability limitations, and interpretability issues. Ethical considerations surrounding transparency, fairness, and clinician autonomy are also critical. The chapter emphasizes the need for user-centered design, regulatory alignment, and real-world validation to ensure safe and equitable integration of AI-CDSS. When thoughtfully deployed, these systems can augment clinical expertise, improve decision accuracy, and enhance patient outcomes across ophthalmic care.