Intracranial aneurysms represent a significant health burden worldwide. The prevalence is approximately 2–3% in the general population with several risk factors having been associated with higher prevalence rates. Rupture can be catastrophic and can lead to high rates of morbidity and mortality. The widespread availability of neuroimaging techniques has facilitated our ability to detect and monitor these aneurysms over time. However, given the limited evidence on natural history, screening for intracranial aneurysms and rupture risk prediction remain active topics of discussion. Therefore, recognition of high-risk features and their integration through a holistic approach are crucial to promptly identify patients at risk for harboring intracranial aneurysms and those with a high rupture risk. In this chapter, we aim to explore the present knowledge on natural history based on population and familial aggregation studies of intracranial aneurysms as well as present screening guidelines. We also discuss some newer vascular and genetic correlates being actively investigated which may inform future evolution of screening guidelines and individualized risk assessment. There is a growing body of literature focusing on studies regarding the use of artificial intelligence and machine learning to leverage vast amounts of morphological, genetic, and computational fluid dynamic data and other risk factors to improve risk prediction.