The article discusses the future of multiscale modeling in physical chemistry, emphasizing its growing importance in addressing complex scientific challenges. Multiscale modeling combines different methods to simulate systems at varying levels of approximation, from quantum mechanics to classical molecular dynamics. This approach has been used for over 60 years and has led to significant advancements. With rapid progress in theory, algorithms, and computing power, multiscale modeling is expected to become a dominant research methodology. The article reviews key approaches such as QM/Continuum, QM/MM, and QM/QM, highlighting their strengths and limitations. It also explores emerging techniques like machine learning and quantum computing, which could enhance multiscale modeling. The text emphasizes the potential of multiscale modeling in simulating complex processes like natural photosynthesis, where simultaneous multiscale calculations on a system with moving domains could provide valuable insights. The article concludes that as computational science advances, multiscale modeling will become more widespread, enabling deeper understanding of physicochemical phenomena and contributing to solving global challenges like climate change and disease control.The article discusses the future of multiscale modeling in physical chemistry, emphasizing its growing importance in addressing complex scientific challenges. Multiscale modeling combines different methods to simulate systems at varying levels of approximation, from quantum mechanics to classical molecular dynamics. This approach has been used for over 60 years and has led to significant advancements. With rapid progress in theory, algorithms, and computing power, multiscale modeling is expected to become a dominant research methodology. The article reviews key approaches such as QM/Continuum, QM/MM, and QM/QM, highlighting their strengths and limitations. It also explores emerging techniques like machine learning and quantum computing, which could enhance multiscale modeling. The text emphasizes the potential of multiscale modeling in simulating complex processes like natural photosynthesis, where simultaneous multiscale calculations on a system with moving domains could provide valuable insights. The article concludes that as computational science advances, multiscale modeling will become more widespread, enabling deeper understanding of physicochemical phenomena and contributing to solving global challenges like climate change and disease control.