Abstract Artificial Intelligence (AI) is transforming physics research by enhancing data analysis, accelerating simulations, improving experimental design, and supporting theoretical discoveries. This review explores recent advances in the integration of AI across major areas of physics, including quantum physics, experimental physics, materials science, high-energy physics, and astroparticle research. Key AI techniques such as deep learning, reinforcement learning, generative models, uncertainty quantification, and explainable AI are examined for their contributions to scientific discovery. The paper also discusses significant challenges related to data quality, reproducibility, transparency, ethics, and responsible innovation. Furthermore, it highlights the growing importance of interdisciplinary collaboration and the integration of AI into physics education and research infrastructure. The review concludes that AI serves as a powerful partner in advancing scientific knowledge, enabling physicists to address increasingly complex problems while emphasizing the need for human oversight and ethical implementation.
B M Patil (Sat,) studied this question.
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