Privacy-Preserving Fall Detection for Elderly Care using Distributed Edge-AI and Pose Estima-tion aims to deliver real-time and reliable fall detection while safeguarding user privacy. Instead of transmitting or storing raw video footage, the system applies pose estimation methods such as OpenPose or MediaPipe to extract human skeletal keypoints, thereby eliminating exposure of sensitive visual information. Lightweight deep learning models, including CNNs combined with LSTM or GRU networks, are deployed directly on edge devices such as smart cameras and IoT nodes, enabling efficient on-device processing. By analyzing temporal posture and motion pat-terns, the system effectively differentiates falls from routine daily activities. Federated learning is incorporated to enhance model performance across devices without sharing raw data. This edge-based approach ensures low latency, minimal bandwidth consumption, and robust data security. Overall, the system provides strong privacy protection, rapid emergency detection, scalability across diverse environments, and dependable operation even under limited network connectivity, making it well suited for continuous elderly monitoring in smart healthcare applications