Non-data mining techniques have been used to analyze malaria data and public health strategies have been implemented based on these analyses to limit the impact of malaria. However, malaria is still prevalent in our communities thus prompting further analyses of malaria data. This study adopts a descriptive data mining approach: association rule mining, to analyze malaria data and investigate relations that can explain the prevalence of malaria. The apriori algorithm was used, after converting the data from numeric to categorical data, and multidimensional association rule measure carried out on generated rules. The minimum support threshold was set at 0.3 and 0.02 for global and local investigations respectively, and minimum confidence set at 0.8 and 0.02 likewise. The results from this study showed a strong association between malaria diagnosis and malaria deaths, among patients aged 5 and above. In addition, synthetic data was generated from the real data using classification and regression trees, and the same investigations carried out. The results from the synthetic data showed a similar trend as that of the real data. Although children below the age of five and pregnant women have been the focus of public health action in my community, this study suggests that attention should also be paid to those above 5 years.