Early detection of leprosy, a neglected tropical disease, is crucial to preventing irreversible nerve damage and disability. Analyzing temperature vari- ations in hands using infrared (IR) cameras offers a potential low-cost alternative to existing medical equipment for early detection of leprosy. This study explores the adaptation of hand landmark detec- tion models, commonly used for hand pose track- ing, to infer the hypothenar area, a critical region for leprosy diagnosis. The research addresses the challenge of limited ground-truth data for the hy- pothenar keypoint by developing annotated datasets and evaluating machine learning models like Lasso Regression and XGBoost. These models signif- icantly outperform the existing method of linear interpolation, demonstrating the feasibility of ac- curate hypothenar keypoint prediction even with limited training data. The findings contribute to the development of accessible, automated tools for early leprosy diagnosis, particularly in resource- constrained settings.