Thriveni Ramya MC
Intelligent methods are becoming more necessary for enterprise data migration to recognize, verify, and move diverse data from outdated systems to contemporary platforms. Data loss, delays, and operational inefficiencies can result from manual and rule-based techniques' frequent struggles with missing documentation, inconsistent formats, unstructured data, and restricted scalability. To identify and migrate intelligent data, this study suggests an AI-driven framework incorporating machine learning, natural language processing, optical character recognition, metadata extraction, Elasticsearch indexing, anomaly detection, and continuous learning. The 500,000 structured database records, 100,000 text documents, and 50,000 text-containing images that made up the experimental dataset represented typical enterprise migration sources. For data identification, the suggested system obtained 98.5% precision, 97.8% recall, and a 98.1% F1-score. The CPU usage was reduced from 70% to 30%, the RAM usage was reduced from 75% to 25%, and the migration time was reduced from six months to three weeks. The scalability tests showed stable accuracy for datasets of 10k to 1M records. The study's thorough and scalable AI-powered migration process improves the accuracy, efficiency, and reliability of digital transformation in structured, unstructured, and image-based enterprise data environments.