Zika virus is a member of the virus family Flaviviridae as of 2016, no medications or vaccines have been developed for the prevention of the disease. It is spread by Aedes mosquitoes which are generally active during daytime. There was a widespread epidemic of Zika fever in 2015, which was caused by the Zika virus in Brazil. It also spread to other parts of North and South America and affected several islands in the Pacific, and Southeast Asia. The Zika virus dataset that we used is a huge dataset containing information about degree of spread of virus at various places in the North and South America, the number and type of cases recorded. In our study, we have performed Gaussian mixture model based clustering to group data points with similar attributes. These clusters can aid in the visualization of the spread of the virus during the epidemic. Entropy assisted ranking reduces the dataset by identifying least important attributes and optimizes the target dataset for higher accuracy and decision making. Gaussian mixture model (GMM) is implemented in spark environment using the machine learning library (MLlib). GMM is a probabilistic model that performs soft clustering by computing the probability of data points and placing them in various Gaussians (clusters). Spark performs parallel distributed processing to mine useful data by distributing datasets and creating resilient distributed dataset (RDD). Apache spark supports in-memory computations and scalability; therefore it works well for iterative algorithms like clustering in GMM.