Background: Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD). However, interpretation of angiographic images is often subject to inter-observer variability and requires substantial expertise. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have introduced novel approaches for automated image analysis, lesion detection, stenosis quantification, and procedural decision support. Objective: This study evaluates the clinical applications of artificial intelligence in coronary angiography analysis and examines its impact on diagnostic accuracy, workflow efficiency, risk stratification, and interventional outcomes. Methods: A multicenter observational study involving 1,250 patients undergoing coronary angiography between January 2020 and December 2024 was conducted. AI-assisted angiographic interpretation was compared with conventional expert assessment. Performance metrics included stenosis detection accuracy, vessel segmentation precision, procedural planning efficiency, and clinical outcomes. Results: AI-assisted analysis demonstrated diagnostic accuracy of 96.4% for significant coronary stenosis detection compared with 92.1% using conventional assessment. Automated vessel segmentation achieved a Dice similarity coefficient of 0.94. AI reduced image interpretation time by 43% and improved inter-observer agreement. Integration of AI-based predictive models enhanced identification of high-risk lesions and future adverse cardiovascular events. Conclusion: Artificial intelligence significantly enhances coronary angiography analysis by improving diagnostic precision, reducing interpretation time, and supporting clinical decision-making. Future integration of explainable AI and real-time interventional guidance may further transform coronary imaging and precision cardiology.