The national department of health is targeting the year 2018 for the elimination of malaria which is mainly endemic in the low altitude (below 1200 m) regions of Mpumalanga, Limpopo and KwaZulu-Natal located in the North-eastern part of South Africa. To develop effective malaria control strategies, it requires the analysis of vector s habitat, detail understanding of the environmental and climatic associates, good knowledge of the socio-demographic factors among others. The aim of this study is to create a model that can integrate remotely derived environmental factors with malaria cases and social (population) factors for effective monitoring and forecasting of incidences of malaria. The aim is to be achieved through set objectives which include; 1) to appraise the use of remote sensing and GIS technologies for malaria study in South Africa, 2) to determine the spatial distribution of mosquito habitats and areas that are prone to epidemics in Nkomazi municipality, 3) to evaluate the link between environmental factors and incidences of malaria and the population at risk using GIS and RS, 4) to predict the seasonal and spatio-temporal variability of incidences of malaria. Results from this study indicated that space and time are key factors in the epidemiology of malaria, to determine spatial and temporal windows of opportunities for elimination strategies. However, there is a limited understanding of the spatio-temporal dynamic of this transmission and of the spatial factors that includes environment, meteorology and social. Until now, satellite earth observation data which provides uniformity, rapid measurements and data continuity that allows for the collection of data over large areas, which cannot be accessed by other means has not been used extensively in the understanding of the spatial-temporal dynamics of malaria in South Africa. In addition, using data from earth and meteorological observing satellites, in particular, Landsat, MODIS and TRMM and notified malaria cases acquired from the malaria control programme in Mpumalanga. This study found that satellite-derived climatic/environmental factors such as Rainfall from TRMM, NDVI, EVI, NDWI and LST from both Landsat and MODIS are associated with malaria incidence. Furthermore, it was found that irrigation activities (agriculture) in the study is largely associated with malaria incidence. In addition, the study found that the economically active population (age 15 64) are the most at risk of malaria infection. The population in Komatipoort village are mostly 4exendangered with lot of imported malaria cases from Mozambique and Swaziland. Seasonal autoregressive integrated moving average models (SARIMA) was developed. The level of prediction, either under-prediction where predicted is less than observed or over-prediction where predicted is greater than observed, are within 10% of the notified malaria cases for all predictions across the 5 villages. Hence, the study, if implemented will strengthen the existing control measures for proper targeting and effective distribution of the scare resources towards malaria elimination and subsequent eradication.