In the contemporary world of global economy real-life data is distributed and evolving consistently. For the purpose of data mining, the large set of evolving and distributed data can be handled efficiently by Parallel Data mining and Distributed Data Mining, Incremental Data mining. In this paper, we discuss about the issues and the present research work that is being carried out on parallel and distributed data mining. Adaptability of some core data mining algorithms such as decision trees, discovery of frequent patterns, clustering, etc., for parallel processing and contemporary research work related to parallel processing of the algorithms is also discussed. We have identified two approaches for carrying out distributed data mining and tried to bring out the advantages of using mobile agents in client server–based approaches, in terms of bandwidth usage and network latency.