The recent advancements in the field of materials science have demonstrated significant progress in leveraging artificial intelligence (AI) and machine learning (ML) for material discovery and design. Large language models (LLMs) are being increasingly adapted for domain-specific applications, such as materials synthesis and property prediction, showcasing their versatility and scalability. These models are eliminating the need for task-specific descriptors, thereby enhancing generalizability and transferability across diverse tasks. Notably, deep learning approaches are proving effective in predicting novel superconducting materials, with experimental validation supporting these predictions. Additionally, ML frameworks are being developed to accelerate the screening of organic molecular additives for perovskite solar cells, leading to the identification of promising candidates with enhanced power conversion efficiencies. These developments highlight the transformative potential of AI and ML in driving innovation and efficiency in materials science research.