The emerging field of computational photonics offers unprecedented opportunities for precision medicine. Building upon a series of foundational works on photonic-energy control and AI-driven medical physics (Articles 19–23), this study introduces a breakthrough therapeutic concept: Crystal-Guided AI Phototherapy (CG-AIP) for personalized oncology. In this approach, adaptive optical crystals are coupled with intelligent algorithms to dynamically modulate the spectral, spatial, and temporal properties of photonic energy. Unlike conventional phototherapy techniques that rely on static wavelength emission and uniform beam profiles, CG-AIP integrates real-time feedback control and crystal-induced beam shaping to generate highly selective photonic fields. These tailored beams can penetrate tissue with controlled depth, concentrate energy in malignant zones, and minimize collateral exposure to surrounding healthy cells. Early theoretical modeling and numerical simulations indicate a remarkable enhancement in tumor selectivity, with energy concentration factors exceeding conventional laser therapy by several orders of magnitude. The integration of tunable photonic crystals with adaptive AI enables non-invasive, patient-specific treatment protocols, paving the way for programmable light-based oncology. This work represents a paradigm shift in the translation of computational photonic principles into clinically deployable medical devices. By combining material science, photonics, and machine learning, CG-AIP establishes the foundation for next-generation oncological interventions, where light is no longer passively emitted but actively guided by intelligent crystalline structures to heal with surgical precision. Keywords: phototherapy, adaptive optics, tunable crystals, personalized oncology, AI-guided medicine, photonics, non-invasive therapy, energy modulation.