Malaria is the leading cause of death in many countries. Numerous studies have been carried out to introduce prevention mechanisms; but most methods employed are limited to mathematical modelling and analysis. Predicting the occurrence of malaria incidence and understanding the dynamics of transmission still remain two key challenges. In this paper, we have utilised two different computational techniques to address these issues. We have used machine learning methods and developed models for predicting the likelihood of malaria outbreak using the incidence data against climatic factors. The success of machine learning depends on the availability of reliable large data sets; but in most cases it is not possible to have reliable and complete data. Also, machine learning does not provide a good understanding of the transmission mechanism. Hence, we have used agent-based modelling approach to simulate the malaria dynamics using some parameters. The model developed has potential to emulate real scenarios for showing impact of the incubation period on malaria dynamics. Moreover, the model can also assist hospitals, public health officials and policy makers with near-real evidence on how malaria infection invading population so as to strategies for feasible intervention.