Advances in machine learning (ML) offer unprecedented opportunities for transforming cardiac care, particularly using imaging data. This talk will explore the intersection of ML, health, and cardiac imaging, focusing on how novel methodologies can drive personalized care and improve clinical outcomes. We will discuss the translation of cutting-edge ML techniques, such as deep learning and transformers, into real-world healthcare systems, specifically in the detection and analysis of coronary anatomy. Additionally, the talk will highlight ongoing research into explainable models, adaptive learning algorithms, and the integration of ML into existing clinical workflows. By addressing challenges like data complexity and generalizability, this presentation will shed light on the path forward for developing AI-powered solutions that not only improve diagnosis and treatment planning but also continuously learn from clinical experience to enhance patient outcomes.