Monte Carlo (MC) photon transport has become a cornerstone in modern medical physics for accurately modeling the stochastic interactions of ionizing radiation within heterogeneous biological tissues. Unlike deterministic approaches, Monte Carlo methods simulate radiation transport through probabilistic sampling of fundamental interaction cross-sections, enabling highly realistic representation of photon behavior in complex anatomical structures. This short communication provides an overview of the theoretical framework of Monte Carlo photon transport, including its statistical foundations, interaction mechanisms, and tissue-specific attenuation characteristics. The major photon interaction processes photoelectric absorption, Compton scattering, and Rayleigh scattering are discussed with emphasis on their energy dependence and relevance to diagnostic imaging and radiotherapy. The role of mass attenuation and energy absorption coefficients in dosimetric accuracy is highlighted, along with the importance of accurate tissue composition modeling. Recent advancements in computational performance, particularly GPU-based acceleration and variance reduction techniques, are also examined, demonstrating their impact on reducing simulation time and improving clinical applicability. Furthermore, the clinical significance of Monte Carlo simulations in radiotherapy planning, imaging optimization, scatter correction, and radiation protection is outlined. The integration of artificial intelligence with Monte Carlo frameworks is identified as an emerging direction for real-time dose prediction and personalized treatment planning. Overall, Monte Carlo methods continue to play a pivotal role in enhancing accuracy, safety, and precision in medical radiation applications.

Keywords: Monte Carlo simulation; Photon transport, Radiation Dose, Cross-sections, Radiotherapy, Compton scattering, GPU computing; Medical physics.