This chapter examines how artificial intelligence (AI) and advanced ocular imaging technologies are transforming systemic disease evaluation through the emerging field of oculomics. The eye, especially the retina, offers a unique non-invasive window into the body’s microvascular and neural health, enabling early detection of systemic conditions such as cardiovascular disease, diabetes, hypertension, kidney disease, and neurodegenerative disorders. Machine learning (ML) and deep learning (DL) algorithms are increasingly used on retinal imaging modalities, including fundus photography, optical coherence tomography (OCT), and OCT angiography (OCT-A), to analyze microvascular features like vessel size, tortuosity, and fractal dimension. These AI models allow for automated measurement and prediction of cardiovascular risk factors, enhancing accuracy and efficiency compared to traditional semi-automated software systems. The chapter showcases landmark studies demonstrating AI’s capability to predict age, blood pressure, cholesterol levels, and coronary artery calcium scores from retinal images, along with innovations such as QUARTZ and transformer-based U-Net architectures for vessel segmentation. Overall, AI-driven ocular biomarkers represent a transformative move toward personalized, non-invasive, and cost-effective systemic risk assessment.