Human motion prediction plays a vital role in real-world applications such as robotics, augmented and virtual reality (AR/VR), human–computer interaction, and intelligent surveillance, where accurate forecasting of future poses improves system safety, responsiveness, and interactivity. Traditional models struggle to balance short-term motion continuity with long-range temporal understanding and spatial joint coherence. In this work, we propose GGTr (Graph–GRU–Transformer), a novel hybrid architecture that integrates Graph Convolutional Networks (GCNs) for spatial dependency modeling, Gated Recurrent Units (GRUs) for short-term temporal dynamics, and Transformer encoders for capturing long-range dependencies. Evaluated on two benchmark datasets, Human3.6M and CMU-MoCap, GGTr demonstrates superior accuracy across multiple prediction horizons, achieving a minimum Mean Per Joint Position Error (MPJPE) of 32.7 mm at 320 ms. Ablation studies validate that the tri-modal integration of GCN, GRU, and Transformer components is crucial for performance. Beyond accuracy, we analyze computational complexity, robustness to noisy/missing joints, and scalability for real-time deployments, highlighting the potential of GGTr for large-scale motion capture systems and embedded applications.