Accurate cephalometric landmark detection is fun damental to orthodontic diagnosis and treatment planning yet manual annotation is time-consuming and subject to inter observer variability. We propose a topology-aware framework for automatic localization of 19 anatomical landmarks on lateral cephalometric radiographs.Our approach leverages a ResNet-50 based feature extractor with a conditional diffusion model that produces anatomically consistent two-channel distance transform maps of bone and soft tissue anatomy. Global craniofacial topol ogy is represented by curve-level topology tokens of the mandible, maxilla, and cranial base, which direct the diffusion model to preserve anatomical consistency. Finally, anatomically consistent distance transforms are utilized by a diffeomorphic atlas-flow module that predicts a stationary velocity field and applies canonical atlas landmarks to patient anatomy using topology preserving diffeomorphisms. Our approach was evaluated using the ISBI 2015 benchmark dataset. Our method achieves a mean radial error of 1.09 ± 0.59 mm for Test1 and 1.22 ± 0.76 mm for Test2, with success detection rates of over 97% at 2.5 mm for Test1 and 93% at 2.5 mm for Test2. Our results show that topology-conditioned diffusion with diffeomorphic atlas warping provides a robust and accurate solution for landmark detection in cephalometric analysis