This presentation summarizes ways in which Analytics, Machine Learning (ML) and Natural Language Processing (NLP) can improve accuracy and efficiency in bio surveillance and public health practices. Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today's applications. Details of a Simulation environment consisting of Devices/Sensors, Web/Mobile, Clinical Records, Internet queries, Social/News media, in which this ML platform was evaluated is also discussed. Infrastructure needs for this operating environment is also covered.