Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient’s head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient’s head and the TMS coil referenced to the individual’s brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient’s head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13246-023-01263-2.