This context is commenced to examine the various methods and its challenges in Disease Identification of Gene Expression Data. The elemental responsibility of these techniques is classification and categorization of gene expression, anal. of the expression, Pattern Recognition, and Identification. This provides an inclusive survey of Micro Array Data anal. techniques and intends a processing component for disease identification. For thehealthcare provider, it is essential to maintain the quality of data because this data is useful to provide cost effective healthcare treatments to the patients. Health Care Administration retains the Microarray data which is refined by expertise and is analyzed by the expertise to identify the disease. This process of analyzing this Microarray data as manual is complicated in identification and classification; due to this Microarray data some difficulties such as missing information, empty values, and incorrect entries. Exclusive of quality information there is no valuableconsequences. For successful data mining, animpediment in health data is individual the majordifficulty for examining medical data. So, it is essential to maintain the quality and accuracy data for data mining to making aneffective decision. The major goal of this survey is focused on various techniques of data mining for developing a prediction model for disease susceptibility using Gene Expression Data.The microarray data is pre-processed to analyze the gene expression to classify the overexpression and under-expression data. Then the classified gene data is then clustered and the best feature selection is applied to discover a pattern. Finally, the association mining handled under the organized set of the gene expression data to theidentification of the disease. This context provides efficient techniques to overcome the manual identification of diseases.