Artificial Intelligence and Machine Learning in Physical Sciences

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming research methodologies in the physical sciences by enabling data-driven discovery, predictive modelling, and accelerated simulations. The increasing availability of high-dimensional experimental and computational data has created significant challenges for traditional analytical and numerical approaches. Machine learning techniques, including supervised learning, deep learning, and physics-informed neural networks, provide powerful alternatives by learning complex nonlinear relationships directly from data while complementing established physical theories. This paper presents a comprehensive review of AI and ML applications across major domains of physical sciences, including physics, chemistry, materials science, astronomy, and earth sciences. Particular emphasis is placed on physics-informed machine learning approaches that integrate governing equations and physical constraints into data-driven models to enhance accuracy, interpretability, and generalization. Key challenges such as data quality, model explainability, and computational cost are discussed. The study highlights emerging trends including autonomous scientific discovery and hybrid theory?data approaches, underscoring the growing role of AI as a foundational tool for advancing modern physical science research.