Artificial Intelligence for Scientific Discovery: A Review Of Methods, Landmark Applications and The Emergence Of Autonomous Research

Artificial Intelligence is transforming how science is done by helping researchers analyse complex data, uncover hidden patterns, generate hypotheses and accelerate discovery across disciplines. This review examines the role of AI in scientific discovery, covering methods, applications, benefits, challenges and future possibilities. Machine learning, deep learning, physics-informed neural networks, equation discovery, natural language processing of scientific literature, reinforcement learning and generative AI are explained in terms of how they support data-driven investigation. Landmark applications are reviewed across structural biology, drug and antibiotic discovery, chemistry, materials science, physics, fusion-energy control, mathematics, astronomy and weather and climate forecasting. The emergence of autonomous laboratories and language-model agents that plan and execute experiments is discussed. A five-layer framework for AI-assisted discovery comprising data, representation, modelling, hypothesis generation and experimental validation is proposed, with research gaps, objectives, a comparison of AI roles and challenges including data quality, interpretability, reproducibility, physical consistency and the risk of over-reliance. The review concludes that AI is most powerful as a partner to scientists, accelerating the cycle of hypothesis, experiment and understanding while human judgement guides what is meaningful.