Modeling and simulation in cardiovascular mechanics has benefited and continues to benefit from four central pillars: (i) advances in imaging, (ii) refined multiscale modeling, (iii) steadily increasing computing power, and (iv) computational modeling in biomechanics, enhanced by data-driven approaches and machine learning. The synergy of these four pillars also facilitates model validation and testing. This short communication attempts to analyze future developments in computational cardiovascular mechanics. Machine learning, for example, will offer better opportunities to combine state-of-the-art imaging, multiscale modeling, and experimental and clinical data into systems whose predictive capabilities far exceed the capabilities of classical computational modeling in cardiovascular mechanics. A particularly promising research approach is hybrid architectures that combine machine learning and classical mechanistic modeling to benefit from the strengths of both. This rapidly growing field could represent a turning point, ensuring that the 2020s and 2030s become the decades in which computational biomechanics is fully translated from academic research into clinical practice, becoming a tool that is as ubiquitous in hospitals as finite element simulations are today in industrial engineering companies.