Zika is an infectious disease and there is a need to detect Zika as soon as possible. The advent of social media provides an opportunity to detect Zika, even before a doctor visit. In this research, we use twitter tweets to detect Zika. A real time Zika virus detection system using neural networks has been developed in this work. We use two different neural networks namely CC4 and MLP. The CC4 neural network helps in detection of Zika that contains previously unknown symptoms and the Multi-Layer Perceptron neural network helps in detection of known symptoms of Zika accurately. The outputs from these two neural networks are used in the classification of Zika. Apache spark is used for real time analysis of twitter data. Once the virus has been detected, the information is useful only if the data is presented in a form that healthcare providers and others can benefit from. We developed three different models namely Geographical, Text and Temporal to visualize the data. Our results show that the Zika virus can be detected with 83% accuracy using twitter data.