Minimum spanning trees are a standout amongst the most essential primitives utilized as a part of graph algorithms. They discover applications in various fields going from scientific categorization of Network design, Approximation algorithms for NP-hard problems, image registration with Renyi entropy, learning salient features for real-time face verification, reducing data storage in sequencing amino acids in a protein and Cluster analysis. In this Paper, we introduce new calculations to process the Kruskal’s Minimum spanning tree (MST) for extensive graphs which don't fit in the memory of a solitary machine. We investigate the hypothetical preparing time under specific presumptions. At long last, we think about the execution of the new calculations on certifiable information utilizing Apache Spark.