Malaria is a significant public health problem and impediment to socioeconomic development in the developing countries. According to World Health Organization (WHO) report, the number of malaria cases increased to 219 million in 2018, two million higher than the number that was reported in 2016. Information on the number of malaria cases is therefore very critical for the design and implementation of malaria control programmes. Malaria surveillance systems can provide important information on the trends in malaria, which varies widely across different seasons of year and socio-economic status. In this study malaria surveillance was conducted year-round in Rusinga Island, Western Kenya using rapid diagnostic test and individuals who tested positive were treated for malaria. The database system comprised of data on house structure and other risk factors relevant to predict malaria. Statistical analysis was conducted to establish the most significant malaria risk factors.