Researchers developed a computational framework that simulates abdominal aortic aneurysm (AAA) growth by modeling elastin degradation and collagen production mechanisms. The system generated 200 virtual aneurysm cases, which were combined with data from 25 real patients to train machine learning models. The LSTM algorithm achieved 92% accuracy predicting maximum aneurysm diameter, while RNN models reached 90% accuracy for diameter and 89% for growth rate predictions. This hybrid approach addresses a critical challenge in cardiovascular medicine: the scarcity of longitudinal imaging data needed to develop robust predictive tools. AAAs affect 4-8% of older adults and can be fatal if they rupture unexpectedly. Current clinical guidelines rely primarily on diameter measurements to assess rupture risk, but growth patterns vary significantly between patients. The virtual patient approach could transform personalized risk assessment by enabling clinicians to predict which aneurysms will grow rapidly and require intervention. However, this is a preprint study that has not undergone peer review, so the findings require validation. The integration of synthetic and real patient data represents an innovative strategy that could be applied to other cardiovascular conditions with limited datasets.