In Africa, malaria is predominantly a rural disease where agriculture forms the backbone of the economy. Various agro-ecosystems and crop production systems have an impact on mosquito productivity, and hence malaria transmission intensity. This study was carried out to determine spatial and temporal variations in anopheline mosquito population and malaria transmission intensity in five villages, representing different agro-ecosystems in Mvomero district, Tanzania, so as to provide baseline information for malaria interventions. The agro-ecosystems consisted of irrigated sugarcane, flooding rice irrigation, non-flooding rice irrigation, wet savannah and dry savannah. In each setting, adult mosquitoes were sampled monthly using light traps recommended by the Centers for Disease Control and Prevention (CDC) from August 2004 to July 2005. A total of 35,702 female mosquitoes were collected. Anopheles gambiae sensu lato was the most abundant (58.9%) mosquito species. An. funestus accounted for 12.0% of the mosquitoes collected. There was a substantial village to village variation and seasonality in the density of Anopheles mosquito population, with peaks in May towards the end of the warm and rainy season. Significantly larger numbers of anophelines were collected from traditional flooding rice irrigation ecosystem (70.7%) than in non-flooding rice irrigation (8.6%), sugarcane (7.0%), wet savannah (7.3%) and dry savannah (6.4%). The overall sporozoite rates for An. gambiae and An. funestus were 3.4% and 2.3%, respectively. The combined overall sporozoite rate (An. gambiae+An. funestus) was 3.2%. The mean annual entomological inoculation rate (EIR) for An. gambiae s.l. was 728 infective bites per person per year and this was significantly higher in traditional flooding rice irrigation (1351) than in other agro-ecosystems. The highest EIRs for An. gambiae s.l. and An. funestus were observed during May 2005 (long rainy season) and December 2004 (short rainy season), respectively. The findings support the evidence that malaria transmission risk varies even between neighbouring villages and is influenced by agro-ecosystems. This study therefore, demonstrates the need to generate spatial and temporal data on transmission intensity on smaller scales taking into consideration agro-ecosystems that will identify area-specific transmission intensity to guide targeted control of malaria operations.