(untitled) (مجلة الفاروق للعلوم, 2026)
This theoretical review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) and machine learning (ML) methodologies in contemporary physics research (Suresh et al., 2024). By synthesizing breakthrough evidence from recent high-impact literature, the study maps the integration of data-driven intelligence across key physical domains, including event classification in particle physics (Wu, 2022), cosmological simulations in astrophysics (Suresh et al., 2024), and the analysis of complex many-body systems via neural-network quantum states (Melnikov et al., 2023). Furthermore, it evaluates the accelerating impact of generative models and informatics in materials design (Batra et al., 2021; Chavez-Angel et al., 2025), alongside the reduction of computational bottlenecks in massive multiscale simulations (Wu, 2025). Beyond specialized disciplines, this paper critically examines the shift toward autonomous scientific discovery, highlighting the emerging paradigm of self-evolving AI systems capable of hypothesis generation and experimental control (Vasudevan et al., 2019). Finally, the study evaluates the underlying technical challenges—specifically the trade-offs between computational efficiency, data quality, and model interpretability—while outlining a comprehensive, physics-informed research framework to guide future hybrid computational discovery (Mishra et al., 2025).