Abstract: The Distributed Data Mining (DDM) is a branch of the field of data mining that offers a framework to mine distributed data paying careful attention to the distributed data and computing resources. Usually, data-mining systems are designed to work on a single dataset. On the other hand with the growth of networks, data is increasingly dispersed over many machines in many different geographical locations. Also, even as most practical data-mining algorithms operate over propositional representations are known as first order learning. In existing system, the concept of knowledge is very important in data mining. In order to get the correct knowledge from the data mining system, the user must define the objective and specify the algorithms and its parameters exactly with minimum effort. If the data mining system produces large number of meaningful information by using a specialized data mining algorithm like association, clustering, decision trees etc., it will take more time for the end- users to choose the appropriate knowledge for the problem discussed. Even choosing the correct data mining algorithm involves more time for the system. Developing a data mining system that uses specialized agents with the ability to communicate with multiple information sources, as well as with other agents requires a great deal of flexibility. The main objective of this paper titled on “An Analysis on Multi-Agent Based Distributed Data Mining System“ describes the knowledge integration, Knowledge Integration in Distributed Data-Mining and Heterogeneous vs. Homogeneous Data-Mining, a literature survey of Multi-Agent Based Distributed Data Mining System, a Model Of Multi –Agent System Based Data Mining, the improving DDM performance by combining distributed data mining and multi-agent system and Data Mining using Multiple Agents.