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Abstract Aerial photogrammetry is a popular non‐invasive tool to measure the size, body morphometrics and body condition of wild animals. While the method can generate large datasets quickly, the lack of efficient processing tools can create bottlenecks that delay management actions. We developed a machine learning algorithm to automatically measure body morphometrics (body length and widths) of southern right whales (Eubalaena australis, SRWs) from aerial photographs (n = 8,958) collected by unmanned aerial vehicles in Australia. Our approach utilizes two Mask R‐CNN detection models to: (i) generate masks for each whale and (ii) estimate points along the whale's axis. We annotated a dataset of 468 images containing 638 whales to train our models. To evaluate the accuracy of our machine learning approach, we compared the model‐generated body morphometrics to manual measurements. The influence of picture quality (whale posture and water clarity) was also assessed. The model‐generated body length estimates were slightly negatively biased (median error of −1.3%), whereas the body volume estimates had a small (median error of 6.5%) positive bias. After correcting both biases, the resulting model‐generated body length and volume estimates had mean absolute errors of 0.85% (SD = 0.75) and 6.88% (SD = 6.57), respectively. The magnitude of the errors decreased as picture quality increased. When using the model‐generated data to quantify intra‐seasonal changes in body condition of SRW females, we obtained a similar slope parameter (−0.001843, SE = 0.000095) as derived from manual measurements (−0.001565, SE = 0.000079). This indicates that our approach was able to accurately capture temporal trends in body condition at a population level.