The IPD-IMGT/HLA Database, established 25 years ago, has been a pivotal resource for the study of human Major Histocompatibility Complex (MHC) genes. Initially released in 1998, the database has evolved to accommodate the increasing complexity and volume of HLA sequence data, driven by advancements in next-generation sequencing (NGS) technologies. The database's core mission is to provide a curated repository of HLA allele sequences, supporting research, clinical decision-making, and commercial developments. Over the years, the database has expanded its capabilities, including the ability to handle full-length genomic sequences and the development of advanced bioinformatics tools. The IPD-IMGT/HLA Database has also adapted to changing user needs, from traditional transplant laboratories to computational users, by integrating application programming interfaces (APIs) and evolving its web infrastructure. Future challenges include managing the growing volume of data, integrating with other databases, and leveraging artificial intelligence to enhance variant analysis. The database's long-term success relies on its ability to remain a reliable and user-friendly resource in the evolving landscape of HLA research and application.The IPD-IMGT/HLA Database, established 25 years ago, has been a pivotal resource for the study of human Major Histocompatibility Complex (MHC) genes. Initially released in 1998, the database has evolved to accommodate the increasing complexity and volume of HLA sequence data, driven by advancements in next-generation sequencing (NGS) technologies. The database's core mission is to provide a curated repository of HLA allele sequences, supporting research, clinical decision-making, and commercial developments. Over the years, the database has expanded its capabilities, including the ability to handle full-length genomic sequences and the development of advanced bioinformatics tools. The IPD-IMGT/HLA Database has also adapted to changing user needs, from traditional transplant laboratories to computational users, by integrating application programming interfaces (APIs) and evolving its web infrastructure. Future challenges include managing the growing volume of data, integrating with other databases, and leveraging artificial intelligence to enhance variant analysis. The database's long-term success relies on its ability to remain a reliable and user-friendly resource in the evolving landscape of HLA research and application.