Malaria is one of the deadliest parasitic diseases common in the warm, moist tropics of the world. Plasmodium falciparum is the causative agent of malaria in human, and it is transmitted to human through the bite of a female Anopheline mosquito especially in Africa. It is estimated that approximately 490 000 deaths from malaria were reported in 2015, with over 90% of such cases reported in Sub-Saharan Africa. The global elimination of this disease is one of the most expensive investments that demands cooperation of various disciplines and entities including health, science, technology, political leadership, finance and general country’s administration. In recent years, attempts were made to characterize the potential breeding sites of Anopheline mosquitoes in order to set baseline for elimination strategies. On the other hand, there is an urgent need to refine malaria mapping methods for updated early-warning and target systems. Mapping of malaria in relation to various environmental and topographical factors is highly advantageous and complements the hospital malaria count and clinical approaches because such environmental-based methods take into account changes in malaria transmission as a result habitat and seasonal and long-term changes in climate and environment. In addition, in very remote areas where there is limited or no access of malaria count data the spatial mapping of malaria becomes crucial for understanding malaria distribution in such isolated areas. The traditional mapping of potential malaria vector habitats is done through field surveys, which are often laborious and time-consuming. The advent of high resolution datasets from earth observation satellites, offers opportunities for accurate mapping of malaria vector habitats for wide areas. The aim of this study was to map the potential habitats of malaria vector (An. arabiensis and An. fenestus) using remote sensing approaches in Vhembe District Municipality of South Africa. Both Landsat TM and Sentinel-2 datasets were used in the study area. Findings from this study indicate that the distribution of P. falciparum is positively correlated to vegetation moisture and greenness. On the other hand, remote sensing data such as that of Sentinel-2 has shown high correlation to the distribution of cattle hoofprints which are known habitats of malaria vectors. It has also beenbig data shown in this study that the remote sensing spectral indices such as those based on broadband reflectances are paramount to characterizing the resting places of An. complex. In an effort to contributing to the indigenous knowledge system (IKS) for repelling malaria vectors common at the study area, this study also showed the feasibility of high spatial resolution Sentinel-2 to map the distribution of Lippia javanica used commonly in the Vhembe District. Findings from this study will give insight into the potential habitats of malaria-causing mosquitoes and aid in efforts aimed at eliminating malaria in the Vhembe District.