Abstract

The rapid growth of data in today’s digital world presents an unprecedented opportunity for research and innovation. Archives, which are repositories of historical and cultural data, hold valuable information that can provide insights into the past. However, the vast amounts of data contained within archives often remain inaccessible or underutilized due to the limitations of traditional search and retrieval methods. Machine learning (ML) technologies offer a promising approach to uncovering hidden knowledge in archival materials. This article explores the role of machine learning in enhancing archival research by improving document indexing, semantic analysis, and pattern recognition. The discussion focuses on the various machine learning techniques being applied to archival data, their potential benefits, and challenges faced in implementation