Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture–recapture (SCR) is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM) offers a cost-effective approach, which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (Panthera pardus) and spotted hyaena (Crocuta crocuta) and compared estimates with conventional SCR methods. Density estimates were low across survey years, when compared to estimates from sub-Saharan Africa, for both leopard (1.9 ± 0.19 sd adults/100 km2) and spotted hyaena (1.15 ± 0.42 sd adults/100 km2). Estimates from SPIM improved precision compared with analytical alternatives. Lion (Panthera leo) and wild dog (Lycaon pictus) were absent from the 2016 survey, but lone dispersers were recorded in 2017 and 2018, and both species appear limited to transient individuals from within the wider transfrontier conservation area. Low densities may reflect low carrying capacity in miombo woodlands or be a result of reduced prey availability from intensive poaching. We provide the first leopard density estimates from Malawi and a miombo woodland habitat, whilst demonstrating that SPIM is beneficial for density estimation in surveys where only one camera trap per location is deployed. The low density of large carnivores requires urgent management to reduce the loss of the carnivore guild in Kasungu National Park and across the wider transfrontier landscape.