Accurate tooth length measurement from dental radiographs is essential for diagnosis, treatment planning, and longitudinal monitoring. Although Deep Learning methods has demonstrated strong potential in medical image analysis, its application for clinically reliable morphometric estimation in routine dental radiography remains insufficiently explored, particularly with respect to measurement agreement. This study addresses this gap by developing a hybrid deep learning framework in which two architectures ar employed: YOLOv8 for tooth detection and Keypoint R-CNN for anatomical landmark localization. The proposed pipeline enables automated Tooth length estimation through sequential object detection and keypoint regression.To the best of our knowledge, this study is among the first to enable automated tooth length estimation for all teeth in a panoramic radiograph using a unified detection–localization framework, unlike previous studies that focus on individual teeth or limited regions. The YOLOv8 model achieved a detection accuracy of 91.26%, while the Keypoint R-CNN attained a localization precision of 94%. Bland–Altman analysis revealed mean bias ranging from -39.82 to +27.65 pixels with narrow limits of agreement and no evidence of proportional bias, indicating stable geometric consistency across varying root lengths. These findings support the feasibility of integrating hybrid detection– localization approaches into routine dental imaging workflows for clinically consistent automated tooth morphometry