The elevated computational cost of high-fidelity cardiac simulations often limits their clinical utility. We developed and validated an efficient cellular automaton (CA) framework to model functional ventricular arrhythmias and handle electrophysiological variability. By embedding the biophysically detailed ToR-ORd model into the CA via action potential duration (APD) and conduction velocity restitution properties, we extend the population-of-models paradigm to a CA environment for the first time. Hypertrophic cardiomyopathy served as a validation exemplar for large-scale simulations of intra- individual variability. The framework accurately captured the electrophysiol- ogical diversity of the underlying biophysical population, with mean average errors of 1.39–6.18 ms (<2.3% relative error) in APD reconstruction. Under complex S1-S5 pacing, the CA reproduces 96% of stimuli with high fidelity (5.9% mean APD error) and captures complex functional re-entrant patterns, such as ventricular tachycardia, consistent with full biophysical simulations. These results demonstrate that the proposed CA model can retain high-fidelity predictive power while enabling rapid, population-based arrhythmia risk assessment at scale across diverse cardiac pathologies.