Computational chemistry and artificial intelligence (AI) have advanced rapidly with the development of high-performance supercomputers and datadriven algorithms. Exponential growth in computing power, exemplified by large-scale systems such as Fugaku and Summit, has enabled increasingly precise molecular simulations. At the same time, machine learning and deep learning approaches, supported by large datasets and robust programming environments, are transforming structure prediction, molecular design, and drug discovery. Landmark achievements such as AlphaFold demonstrate the potential of AI to solve complex biological problems with unprecedented accuracy. The integration of supercomputing and AI is accelerating innovation across chemistry, medicine, and materials science. Despite challenges related to data dependence and model interpretability, continued technological progress is expected to further expand the role of AI-driven computational methods in scientific research and practical applications.