Master Data Management (MDM) ensures data integrity, consistency, and reliability across an organization's systems. I introduce a novel complex match and merge algorithm optimized for real-time MDM solutions. The proposed method accurately identifies duplicates and consolidates records in large-scale datasets by combining deterministic matching, fuzzy matching, and machine learning-based conflict resolution. I implemented it using PySpark and Databricks; the algorithm benefits from distributed computing and Delta Lake for scalable and reliable data processing. Comprehensive performance evaluations demonstrate a 90% accuracy on datasets of up to 10 million records while maintaining low latency and high throughput, significantly improving upon existing MDM approaches. The method shows strong potential in domains such as healthcare and finance, with an overall 30% improvement in latency compared to traditional MDM systems.