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An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics and the increasing availability of computerized geographic information system (GIS) technology. This technique is known as “Disease Clustering” and using this information for mapping and future prediction is termed as “Geo-Spatial Disease Clustering”. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of hard copies, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different classes like age group, gender, provider (doctors), region, etc. During this research this data is used for the experiments and testing. Variety of techniques and algorithms have been proposed in the literature for disease mapping. The effectiveness of these techniques may vary with the varying types, volume, structure of data and disease of interest. In this research, investigation of data visualization techniques for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance for patient and healthcare providers and government bodies. By this, we can extract data that describes the association of disease with respect to age, gender, and location. Moreover, temporal analysis helps earlier identification of disease, to be care of and necessary avoiding arrangements can be taken.