Machine learning (ML) has become a transformative tool in scientific research, particularly in the physical sciences. This review explores the intersection of ML and physics, highlighting recent developments, applications, and challenges. ML encompasses a wide range of algorithms and techniques for data processing, now widely used across scientific disciplines. The review covers key areas where ML has made significant contributions, including statistical physics, particle physics and cosmology, quantum many-body systems, quantum computing, and chemistry and materials science. In statistical physics, ML methods have been used to understand complex systems, with insights from physics informing ML algorithms and vice versa. For example, statistical physics concepts help explain the behavior of deep learning models, such as the phase transitions in learning and the glassy nature of deep learning landscapes. ML techniques, in turn, have been applied to problems in statistical physics, such as data clustering and generative modeling. In particle physics and cosmology, ML is used for tasks like jet physics, neutrino detection, and photometric redshift estimation. ML also plays a role in solving inverse problems and likelihood-free inference, which are crucial in data analysis. Generative models, such as autoencoders and generative adversarial networks (GANs), are increasingly used to simulate and analyze complex data. In quantum many-body physics, ML is used to model quantum states and simulate many-body systems. Neural network quantum states and tensor networks are being explored to improve the efficiency of quantum simulations. ML is also being applied to quantum computing, where it helps in tasks like quantum state tomography and error correction. In chemistry and materials science, ML is used to predict material properties, such as electron densities in density functional theory, and to generate datasets for simulations. ML is also being used to accelerate the discovery of new materials and chemical compounds. The review also discusses the development of novel computing architectures to accelerate ML, including classical and quantum hardware. These architectures aim to improve the efficiency and performance of ML algorithms, particularly in handling large-scale data and complex models. Overall, the intersection of ML and physics is a rapidly evolving field with significant potential for future advancements. The review highlights the importance of interdisciplinary collaboration and the need for further theoretical and practical research to fully harness the power of ML in scientific discovery.Machine learning (ML) has become a transformative tool in scientific research, particularly in the physical sciences. This review explores the intersection of ML and physics, highlighting recent developments, applications, and challenges. ML encompasses a wide range of algorithms and techniques for data processing, now widely used across scientific disciplines. The review covers key areas where ML has made significant contributions, including statistical physics, particle physics and cosmology, quantum many-body systems, quantum computing, and chemistry and materials science. In statistical physics, ML methods have been used to understand complex systems, with insights from physics informing ML algorithms and vice versa. For example, statistical physics concepts help explain the behavior of deep learning models, such as the phase transitions in learning and the glassy nature of deep learning landscapes. ML techniques, in turn, have been applied to problems in statistical physics, such as data clustering and generative modeling. In particle physics and cosmology, ML is used for tasks like jet physics, neutrino detection, and photometric redshift estimation. ML also plays a role in solving inverse problems and likelihood-free inference, which are crucial in data analysis. Generative models, such as autoencoders and generative adversarial networks (GANs), are increasingly used to simulate and analyze complex data. In quantum many-body physics, ML is used to model quantum states and simulate many-body systems. Neural network quantum states and tensor networks are being explored to improve the efficiency of quantum simulations. ML is also being applied to quantum computing, where it helps in tasks like quantum state tomography and error correction. In chemistry and materials science, ML is used to predict material properties, such as electron densities in density functional theory, and to generate datasets for simulations. ML is also being used to accelerate the discovery of new materials and chemical compounds. The review also discusses the development of novel computing architectures to accelerate ML, including classical and quantum hardware. These architectures aim to improve the efficiency and performance of ML algorithms, particularly in handling large-scale data and complex models. Overall, the intersection of ML and physics is a rapidly evolving field with significant potential for future advancements. The review highlights the importance of interdisciplinary collaboration and the need for further theoretical and practical research to fully harness the power of ML in scientific discovery.