Drought is a natural phenomenon that starts slowly and spread equanimity and traces severily on all human activities. Therefore, complete recognition and exact monitoring of drought can provide appropriate tools for dealing with it and decreasing damaging effects. One of the strategic areas that are very important in terms of agriculture is Khuzestan province. This province is very remarkable due to its permanent rivers, flood-prone rivers also various reservoirs. The main objective of this study is to improve drought monitoring by finding dependencies between drought and several oceanic and climatic parameters in different approach in comparison with statistical correlations. In this research used Data Mining Techniques of Association Rules. Drought events were determined according to Standard Precipitation Index (SPI) and its Dependencies were surveyed using oceanic- climatic indices such as Southern Oscillation Index (SOI), Pacific/North American (PNA) Index, Multivariate ENSO Index (MEI), Pacific Decadal Oscillation (PDO) Index and North Atlantic Oscillation (NAO). Results showed that the classes of selected patterns which are dominant on drought are similar in different time delays. It means that drought events are compeer with normal status of indices and are predictable with maximum and minimum accuracy 74.24 and 44.86 percent, respectively. Therefore, these rules can use as a supplement to existing approaches for drought monitoring.