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Control theory underpins the stabilization of dynamic systems, including cardiac tissue, where disruptions in electrical conduction cause arrhythmias. Current treatments either act rapidly but without precision or deliver targeted interventions that cannot adapt in real time. We present an integrated platform combining optical voltage mapping (OVM), machine learning (ML), and optogenetics for autonomous, real-time detection and correction of cardiac rhythm disorders in vitro. OVM provides high-resolution membrane potential visualization; the ML module identifies arrhythmic events and drives microLED-based light patterns restoring normal conduction; and optogenetics enables light-based modulation of excitable cells. This integration of electrical, optical, and bioelectrical domains through a unified computational control layer enables adaptive, closed-loop rhythm stabilization, a significant advance in real-time electrophysiological interventions. Because inference and actuation run in real time on modest hardware, the same control loop could be embedded into miniaturized devices or microcontrollers, accelerating the transition from in-vitro to in-vivo automated rhythm management.