Rift Valley fever (RVF) is a vector-borne, viral, zoonotic disease that threatens human and animal health. In Kenya the geographical distribution is determined by spread of competent transmission vectors. Existing RVF predictive risk maps are devoid of vectors interactions with eco-climatic parameters in emergence of disease. We envisage to develop a vector surveillance system (VSS) by mapping the distribution of potential RVF competent vectors in Kenya; To evaluate the correlation between mosquito distribution and environmental-climatic attributes favoring emergence of RVF and investigate by modeling the climatic, ecological and environmental drivers of RVF outbreaks and develop a risk map for spatial prediction of RVF outbreaks in Kenya. Using a cross-sectional design we classified Kenya into 30 spatial units/districts (15 case, 15 control for RVF) based on historical RVF outbreaks weighted probability indices for endemicity. Entomological and ecological surveillance using GPS mapping and monthly (May 2013- February 2014) trapping of mosquitoes is alternatively done in case and control areas. 2500 mosquitoes have been collected in 15 districts (50% geographical target for each for case and control). Species identified as (Culicines-86%, Anophelines-9.7%, Aedes- 2.6%) with over 65% distribution in RVF endemic areas. We demonstrate the applications of spatial epidemiology using GIS to illustrate RVF risk distribution and propose utilizing a Maximum Entropy (MaxEnt) approach to develop Ecological Niche Models (ENM) for prediction of competent RVF vector distributions in un-sampled areas. Targeting RVF hotspots can minimize the costs of large-scale vector surveillance hence enhancing vaccination and vector control strategies. A replicable VSS database and methods can be used for risk analysis of other vector-borne diseases.