Cardiogenic shock (CS) is a life-threatening cardiovascular condition characterized by critically reduced cardiac output and systemic hypoperfusion. Effective management of CS requires a holistic understanding of the interplay between mechanical circulatory support (MCS) devices, pharmacological therapies, and patient-specific pathophysiological responses. This study leverages a multiscale computational modeling framework to integrate these complex interactions across multiple physiological scales, from cellular mechanisms to organ-level dynamics and systemic circulation. The model incorporates the Pulse computational framework [1], which allows for the seamless simulation of physiological processes and their interactions. MCS devices, such as percutaneous ventricular assist devices (pVADs), left ventricular assist devices (LVADs), intra-aortic balloon pumps (IABPs), and extracorporeal membrane oxygenation (ECMO), are evaluated to assess their contributions to hemodynamic stabilization. Additionally, pharmacological therapies are represented through detailed pharmacokinetic (PK) and pharmacodynamic (PD) models to quantify their systemic effects and therapeutic potential. To further enhance the analysis, Gaussian Process Emulators (GPEs) are employed, as demonstrated in Longobardi et al. [2]. These tools facilitate global sensitivity analysis and patient-specific population studies, providing insights into the variability and uncertainty of treatment outcomes. Moreover, the multiscale framework benefits from established mathematical approaches such as those described in Müller and Toro [3], which are essential for capturing the interconnected dynamics of circulatory, cardiovascular, and systemic functions. This multiscale perspective is crucial for understanding the holistic effects of combined interventions, as it provides a comprehensive view of how localized actions, such as device operation or drug delivery, propagate through the cardiovascular system and impact systemic physiology. The patient-specific calibration of these models allows for the prediction of individual therapeutic responses, minimizing trial-and-error approaches and improving clinical decision-making in Intensive Care Units (ICUs). The findings underscore the transformative potential of multiscale computational modeling in optimizing CS management and advancing personalized medicine.