Out-of-hospital cardiac arrest (OHCA) remains a leading cause of premature mortality worldwide, with rising incidence in ageing populations and persistently low survival despite three decades of resuscitation science advances. The post-cardiac-arrest patient with hypoxic-ischaemic brain injury, hemodynamic instability requiring escalating vasopressor and inotrope support, and uncertain neurological prognosis represents one of the most cognitively demanding scenarios in critical care medicine. Outcomes depend on rapid integration of multi-modal data streams — invasive hemodynamics, ventilator parameters, electroencephalographic activity, and metabolic markers including lactate. The existing academic machine-learning literature has made substantial progress in single-modality post-arrest prognostication, but comparatively little work has integrated these modalities into a single, explainable, clinically deployable decision support layer. Recent multi-modal modelling work confirms the trajectory toward integration. In high-resource tertiary settings, this multi-modal integration is performed cognitively by a dense layer of specialist human capital; in resource-variable tertiary settings across many low- and middle-income countries, the same monitoring equipment is increasingly available but the specialist integration layer is thinner, and the foundational epidemiological data infrastructure required to validate AI models is uneven and incompletely developed. This perspective synthesises the existing commercial and academic landscape, identifies three structural gaps (continuous metabolic monitoring, closed-loop drug titration, and multi-modal integration), and proposes a conceptual architecture for an integrated post-arrest critical care Software-as-a-Medical-Device with explainable tiered output and embedded Utstein-aligned registry capture. The architecture is offered as a design specification, not as an existing implementation. The article explicitly addresses limitations of current single-modality machine-learning evidence, dataset shift across deployment settings, and the validation sequencing required before clinical deployment.