Computational modeling in biomedicine is crucial for understanding complex physiological and pathological processes. Phenomena such as cardiovascular function, brain activity, and the respiratory system, result from the interplay of various physical processes (blood flow, tissue contraction, conduction of electrical signals, etc, …), which span multiple spatiotemporal scales and require different levels of physical fidelity. As a result, the mathematical representation of these interdependent events involves multiphysics and multiscale systems, demanding the development of advanced mathematical models and the design of efficient, reliable, and robust numerical methods for accurate simulations. The availability of large datasets and the request for near-real-time personalized predictions have driven advances in applied mathematics, particularly in approaches merging Data Science and Scientific computing. Techniques such as Gaussian Processes, Neural Networks, and Physics-Informed Machine Learning are transforming computational medicine by enabling efficient solutions to inverse problems, lowering computational costs of high-fidelity simulations, and thus enhancing the predictive capabilities of the models. These advancements may not only accelerate clinical translation but also support the development of innovative medical technologies such as implantable devices and personalized and optimized diagnosis and treatment strategies. This minisymposium welcomes original contributions in the field of computational medicine. This includes novel approaches to mathematical modeling, numerical methods, and Scientific Machine Learning for simulating the complex dynamics of physiological systems, such as the cardiovascular, respiratory, or brain functions; high-performance computing; and techniques for data assimilation and subject-specific computational models. Furthermore, it aims at offering a valuable opportunity for academics and professionals to actively share their latest findings, exchange insights, and establish connections that will inspire future collaborations and accelerate research in this dynamic field.