Malaria is a disease with a complex transmission cycle, influenced by biophysical and socio-ecological drivers. While malaria is the most studied mosquito-borne disease in the context of climate change, there are limited studies that model the many inter-related pathways of causality and hierarchical relationships between these drivers of transmission. Thus, model projections still contain a high degree of uncertainty. We address this uncertainty using a participatory systems approach and Bayesian Network modelling. We develop the model using key drivers of malaria transmission. We parametrize the BN model using quantitative and qualitative data from global climate change models, Demographic and Health Surveys, Malaria Indicator Survey, Expert knowledge, key informant interviews and focus group discussions to provide a robust assessment of malaria risk. We estimate the posterior probability of risk of malaria infection given conditional prior probabilities under different climate change scenarios to suggest potential adaptation options for risk management. Results of our sensitivity analysis indicate that under baseline conditions, health system services, agricultural land use and sand quarrying are significant drivers of optimum malaria transmission at the community level. Under RCP4.5 projected to 2080, air temperature and to a lesser extent, sand quarrying, health system services and agricultural land use are the significant drivers of malaria risk. Under RCP8.5 projected to 2080, air temperature is the significant driver of optimum malaria transmission. Our results suggest that in the short-term, interventions to manage climate change and malaria transmission risk at the community level should focus on health systems strengthening and sustainable land use practice while in the long term, focus should shift to managing the risks associated with higher temperatures. Our results highlight which adaptation interventions are likely have the most influence on risk reduction under changing climate and have important implications for climate change and health adaptation policy and practice.