ABSTRACT The contemporary intensive care unit generates an overwhelming magnitude of high‐frequency, granular physiological data. However, conventional decision‐making for patients requiring temporary mechanical circulatory support (tMCS) remains tethered to reactive, threshold‐driven manual adjustments, underutilizing continuous data streams and failing to optimize the complex patient–machine interaction. This review examines the integration of deep neural networks (DNNs) as a candidate strategy for advanced clinical decision support. By identifying a continuous “digital phenotype,” hierarchical deep learning architectures signal a shift toward real‐time trajectory recognition in tMCS. We establish a four‐tier functional taxonomy—prognosticative, descriptive, predictive, and prescriptive—to categorize translational milestones from automated pump‐speed optimization to closed‐loop homeostatic regulation. Operational challenges are analyzed through the TEIP (Transparency, Explainability, Interpretability, and Performance) Core‐Lens Framework, navigating the dynamic equilibrium between computational density and clinical explainability. We further evaluate the evolving global regulatory landscape, advocating for a foundation on verified empirical safety and rigorous clinical trial validation over absolute mathematical transparency in high‐density architectures. Implementing a strategic roadmap including context‐specific validation, multimodal integration, and human‐centered design is quintessential to safely transform passive hardware into intelligent systems. As the field evolves, DNN technology offers a consequential opportunity to advance early cardiopulmonary failure recognition into a clinical reality.