This research introduces a photon-assisted AI framework for real-time personalized drug optimization, designed to continuously adapt therapeutic strategies according to the patient’s molecular response profile. The system operates by coupling quantum-photonic computation with adaptive deep learning models, enabling ultrafast pharmacological simulations that predict the most effective dose, molecular combination, and administration timing with unprecedented precision. Unlike conventional pharmacokinetics, which rely on static, population-based models, this new approach leverages dynamic quantum feedback loops between biological signals and photonic computation units. These loops capture and process biophotonic signatures emitted by living tissues, translating them into real-time data streams that guide AI-driven therapeutic adjustments. At the core of the system lies a photonic neural processor capable of performing energy–information transformations at the speed of light. This allows the model to evaluate millions of possible molecular interactions per second, optimizing pharmacodynamics and pharmacogenomics simultaneously. The resulting output is a continuously evolving “therapeutic intelligence” — a digital twin of the patient’s biological system that learns, adapts, and prescribes autonomously under medical supervision. This fusion of computational photonics and bioadaptive AI heralds a paradigm shift in medicine: from predictive algorithms to responsive, self-adjusting therapies, where treatment evolves as rapidly as the biology it seeks to heal. Keywords: AI, photonics, personalized medicine, drug optimization, quantum pharmacology, real-time therapy, computational photonics.