This paper explores the challenges and solutions for name matching across multiple databases in India, a country characterized by its vast cultural and linguistic diversity. The problem of name searching and matching is the core of entity name recognition in text analytics, data mining, information extraction, and search engines. Several methods for comparisons and duplicate detection have been employed to compare names within a database. While string comparison algorithms are typically used for text comparison, personal names possess distinct characteristics that set them apart from standard text. This paper proposes a complete pipeline for name matching that includes name standardization and name encoding using Soundex. We analyzed and consolidated the datasets for the Land Records and two national-level government schemes for four Indian states. Different string similarity metrics are explored to generate features for the XGBoost model. The proposed model matched the names across the test dataset with 99.68% accuracy, precision score as 0.99, and f1 score as 0.99.