Reliable population estimates of animal density is one of the most elementary needs for the control and management of wildlife, particularly for introduced ungulates on oceanic islands. On Guam, Philippine deer (Rusa marianna) and wild pigs (Sus scrofa; wild boar and descendants of domestic pigs) cause agricultural and ecological damage and are hunted for recreational, nutritional, and cultural uses. Most common population estimation methods are based on capture-recapture and related methods that require marking or uniquely identifying individuals. Capturing, marking, and either recapturing or resighting individuals repeatedly is labor intensive and expensive. In many situations marking or individually distinguishing animals is not feasible, necessitating estimating densities and abundance from unmarked animal populations. Motion-triggered camera traps are a relatively low-cost approach that can be used to generate presence/pseudo-absence and indices of relative abundance on multiple species simultaneously. We used distance sampling with camera traps to estimate deer and pig densities from non-independent observations of unmarked animals while accounting for imperfect detection where some present individuals are not detected. We present methods to (1) process the digital imagery data automatically for species detection and species categorization using a machine learning algorithm, (2) automatically estimate distance to detected species using a separate machine learning algorithm, and (3) estimate densities using distance sampling with camera trap methods. We compare accuracy statistics and results of ungulate densities estimated from automated methods to those estimated from manual assessment. We collected 7,695 videos: 381 videos contained deer and 377 contained pigs. The object detection and identification model performed well with overall accuracy above 80% and F1 scores above 0.9. The hazard-rate key detection function was chosen for deer and pigs based on Akaike’s information criterion accounting for overdispersion. Deer density estimates were 0.53 ± 0.20 deer/ha with higher density in the Plateau area than the Tarague area of Guam. Pig density estimates were 0.53 ± 0.32 pigs/ha, also with higher densities in the Plateau area than the Tarague area. Coefficients of variation ranged from 0.38 to 1.15, and greater numbers of camera traps would be required for pigs than deer to achieve desired coefficients of variation. On average, 101.9 ± 82.3 deer and 131.6 ± 118.8 pigs were detected per day. Microsite heterogeneity affected densities where orientation-specific estimates were less precise than estimates made with the full dataset. We developed a camera trap survey design based on standard camera trapping sampling protocols using motion-activated, digital cameras and determined that distance sampling methods using camera traps produce reliable densities of unmarked deer and pigs on Guam. Our camera trap survey design is based on a regularly sized trapping grid that is generalizable and can be expanded to survey other areas of Guam.