Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients.