Leprosy remains a significant health challenge in developing countries, where early diagnosis is crucial to prevent severe disabilities and social stigma. Recent studies have shown that infrared imaging can be used to detect abnormalities associated with leprosy by analyzing hand temperature variations. However, existing diagnostic methods relying on manual annotation of thermal images are timeconsuming, lack standardization, and require technical expertise. This research investigates methods for implementing real-time infrared video-based temperature analysis on mobile devices by focusing on hand landmark detection models, model optimization techniques, and evaluation metrics. A comprehensive literature review identified promising models such as MediaPipe Hands, OpenPose, and YOLO variants for hand landmark detection, along with optimization methods like pruning, quantization, and Neural Architecture Search (NAS) to adapt these models for mobile deployment. Furthermore, evaluation frameworks incorporating both performance and capability-oriented metrics were examined to ensure efficient and reliable deployment on resource-constrained devices. This study provides insights into developing a fully automated, mobile-based diagnostic tool for early leprosy detection, highlighting the challenges and opportunities in adapting visual AI models for infrared analysis. Future research should focus on empirical validation of optimized models on mobile platforms.