Cardiovascular magnetic resonance (CMR) has seen progressive adoption in clinical practice, as reflected in European and American cardiology guidelines. Its ability to assess a wide range of cardiovascular pathophysiological processes, including edema, fibrosis, ischemia, and viability, makes it a valuable diagnostic tool. However, prolonged acquisition times and patient-dependent limitations hinder its broader clinical integration. To address these challenges, strategies such as streamlined protocols, “all-in-one” imaging, and real-time acquisitions have been proposed. While these approaches show promise, technical constraints still limit their clinical implementation. Artificial intelligence (AI) has emerged as a transformative tool in CMR, enhancing image acquisition, reconstruction, segmentation, and analysis. This chapter explores AI-driven techniques designed to optimize CMR workflows, improve image quality, and accelerate acquisition times, potentially redefining its role in cardiovascular diagnostics.