Abstract Sudden cardiac arrest remains a leading cause of mortality worldwide, largely due to delayed detection and intervention. Most existing monitoring systems identify cardiac arrest only after circulatory collapse has already occurred, significantly limiting the effectiveness of emergency response. This study presents the design and numerical validation of an AI-based early cardiac arrest detection system capable of predicting imminent cardiac arrest prior to its onset. The proposed framework integrates non-invasive physiological sensing with a hybrid physics–artificial intelligence approach. Blood flow dynamics are modeled using the incompressible Navier–Stokes equations, while oxygen transport is represented by a convection–diffusion–reaction model to capture the progressive development of hypoxia under pre-arrest conditions. Numerical simulations are conducted to investigate hemodynamic instability and oxygen depletion patterns associated with declining cardiac output. Key outputs from the numerical model, including velocity fields, oxygen concentration gradients, and a derived hypoxia index, are combined with physiological signals and processed by a machine learning–based prediction engine. The results demonstrate that the proposed system successfully identifies critical pre-arrest signatures and provides early warning within a clinically meaningful time window. This work establishes a robust foundation for predictive cardiac monitoring and highlights the potential of physics-informed AI to improve survival outcomes, enhance emergency medical decision-making, and support the future development of intelligent, real-time cardiac arrest detection devices.