Cholera is reported as endemic in more than 50 countries, many of which are in sub-Saharan Africa. Nigeria currently reports the second highest number of cases, with several risk factors potentially contributing to this including poverty, water, sanitation and climate. Enteric pathogens have a significant global burden, especially on children and those most vulnerable. Despite this, attention is often drawn away from these diseases, most recently to Ebola and COVID-19. To address the need for more research and focus on cholera, a covariate selection process and machine learning was used. Data for environmental (floods, droughts) and social (conflicts) extremes, along with pre-existing social vulnerabilities, were fit to time varying reproductive number in Nigeria. We analysed this both spatially and temporally and used it to create a traffic-light system for cholera transmission, highlighting potential thresholds and triggers for outbreak. Improved access to sanitation, number of monthly conflict events, Multidimensional Poverty Index and Palmers Drought Severity Index were retained in the best fit model. Varying exposure periods showed that those living in decreased poverty, with more access to sanitation were not as vulnerable to changes and offset some of the cholera risk caused by extremes. The work presented here shows the need to address these pre-existing vulnerabilities and sustainable development for disaster prevention and mitigation and improve health and quality of life.