In India Japanese Encephalitis (JEV) has been a major public health problem. In endemic districts of country each year there is a large-scale outbreak occurring of JEV. Research says that Japanese Encephalitis is a flavivirus related to West Nile Virus, Yellow Fever and Dengue and it is escalated by mosquitoes. Japanese Encephalitis is although rare, but the fatality rate is around 30%. Till now there is no cure for JEV, the entire treatment is focused for supporting the patient to overcome disease and relieving severe clinical sign. Maximum number of JEV cases in India are of infants and the fatality rate is around 30% which is a great matter of concern. Here Force of Infection denotes the rate at which sensitive individuals acquire an infectious disease. In India, states which report major outbreak of Japanese Encephalitis are Uttar pradesh, Andhra Pradesh, West Bengal, Karnataka, Assam, Tamil Nadu, Bihar, Goa and Manipur. The impacting factors include Climate, Rice Distribution, Livestock Distribution, Population Density, Specific Age Group Density, Urban/Rural Category and Elevation. Impacting Factors may change with the location. Here we have used Machine learning algorithms like Ridge Regression, Lasso Regression, ElasticNet Regression and Multi-layer Perceptron for the prediction of Force of Infection of Japanese Encephalitis Virus. ElasticNet Regression Algorithm is also used for extracting the significant attribute from the JEV Dataset. The proposed model generated an optimum performance in context to the error rate and accuracy of prediction.