In highly dynamic and interactive vehicular environments, accurate and efficient trajectory prediction is critical for driving safety and traffic efficiency. To overcome the limitations of existing methods, including degradation of long-range historical context, high computational cost for long sequences, attention dispersion in spatial interactions, and limited explicit modeling of future interactions, this paper proposes a Hierarchical Mamba Graph Attention Network (HM-GAT) for vehicle trajectory prediction. A Mamba-based selective state-space model (SSM) encoder with linear complexity is employed to capture nonlinear temporal dynamics and transient maneuvering behaviors. For spatial modeling, a heterogeneous topology-driven dual-layer graph attention mechanism, combined with adaptive sparse filtering and a foresighted future graph, enables precise identification of neighboring vehicles with potential collision risks. The proposed architecture integrates global intention exploration, topology bridging, and residual refinement in a coarse-to-fine manner, achieving effective spatio-temporal decoupling. Experimental results show that HM-GAT reduces the average RMSE by 33.1% relative to the best-performing baseline on NGSIM, achieves competitive prediction accuracy on Argoverse 2 without HD-map priors, and achieves lower inference latency than the compared baseline methods on both datasets.