Materials informatics, an emerging discipline at the intersection of materials science and information technology, has revolutionized the way materials are discovered, designed, and deployed. By harnessing advanced computational tools, algorithms, and large datasets, this field offers unprecedented capabilities in predicting material properties, optimizing manufacturing processes, and enhancing sustainability. This review presents an in-depth examination of the core methodologies of materials informatics, including machine learning, data acquisition techniques, and prominent databases. It further explores significant applications and case studies that demonstrate the impact of materials informatics on material discovery and optimization. Challenges such as scalability, data integration, and quality are discussed, highlighting the technical and practical hurdles in the field. The review also identifies emerging trends like quantum computing and artificial intelligence that promise to push the boundaries of materials informatics. Additionally, the pivotal role of this discipline in promoting sustainable material development is underscored, illustrating its potential to significantly reduce environmental impacts through innovative material solutions. This paper aims to provide a comprehensive overview of materials informatics, offering insights into its current achievements and future directions in fostering technological and sustainable advancements in material science.