Lymphatic Filariasis (LF), a parasitic nematode infection, often resulting in disability poses huge economic burden to affected countries. To meet eradication deadlines in line with the global Neglected Tropical Diseases elimination and health system strengthening goals, novel strategies are needed to complement existing approaches. LF endemicity is localized and prevalence, spatially heterogeneous. Species distribution models (SDMs) can help identify subtle differences in risk factors that influences the transmission of LF in geographically distinct regions. Thus in this contribution, presence absence records of microfilaria (m)f in Ghana were stratified into Northern and Southern Zones and used to run SDMs, whilst climate, socioeconomic and land coverage variables provided explanation information. GLM (Generalized Linear model), GBM (Generalized Boosted Model), ANN (Artificial Neural Network), SRE (Surface Range Envelope), MARS (Multivariate Adaptive Regression Splines) and RF (Random Forests) algorithms were run for both study zones and also for the entire country for the purpose of comparison. Best model quality was obtained with RF and GBM algorithms with highest AUC between 0.98 and 0.95, respectively. The models predicted high suitable environments for LF transmission in the short grass savanna areas in the northern and along the coastal southern parts of Ghana. Mainly, land cover and socioeconomic variables such as, proximity to inland waterbodies and population density uniquely influenced LF transmission in the South while poor housing was a distinctive risk factors in the North. Precipitation, temperature, slope and poverty were common risk factors but with subtle variations in response values, which was confirmed by the countrywide model. This study has demonstrated that, an understanding of the geographic distinctness in risk factors is required to inform the development of area-specific transmission control systems towards LF elimination in Ghana and internationally.