Coronary artery disease (CAD) remains a significant public health concern due to its high morbidity and mortality rates. Early detection and timely evaluation are crucial for improving patient outcomes. While both invasive and non-invasive methods are available for assessing CAD risk, non-invasive approaches minimize the complications associated with invasive procedures. Over the past two decades, advancements in artificial intelligence (AI), particularly machine learning techniques such as deep learning and natural language processing, have revolutionized cardiology. These technologies enhance diagnostic accuracy and clinical efficiency in non-invasive CAD evaluation. However, the broader adoption of AI faces critical challenges, including ethical concerns such as data privacy, high computational costs, and resource allocation disparities. This article explores the current landscape of non-invasive CAD assessment, highlighting the transformative potential and associated challenges of AI integration.