The convergence of photonics and artificial intelligence (AI) marks the beginning of a new era in computational pharmacology. Traditional in silico drug design, while powerful, remains fundamentally constrained by the sequential nature and limited processing speed of electronic computation. Molecular interactions—complex, multidimensional, and dynamic—often require extensive time and energy to simulate, delaying the path from molecular hypothesis to therapeutic validation. This study introduces the Photonically-Assisted AI Drug Design Pipeline (PAI-DDP), a hybrid computational framework that integrates photonic computation for ultrafast simulation of molecular interactions and AI-driven algorithms for predictive optimization of drug candidates. In this system, photons replace electrons as carriers of information, enabling parallel data processing at the speed of light, while deep learning models interpret, classify, and refine molecular configurations in real time. Preliminary computational assessments demonstrate that PAI-DDP can accelerate molecular simulation by one to two orders of magnitude, dramatically reducing design time while enhancing predictive precision. The synergy between light-based modeling and machine intelligence enables data-driven molecular generation, where candidate molecules are not merely simulated but intelligently evolved based on physicochemical and biological constraints. Ultimately, this research represents a paradigm shift toward autonomous and adaptive drug discovery. By fusing photonic computation with artificial intelligence, the PAI-DDP framework establishes the foundation for next-generation precision pharmacology, capable of designing, testing, and optimizing therapeutic compounds in seconds — a step closer to real-time, patient-specific medicine.