Heterogeneous Graph Neural Networks have emerged as powerful tools for analyzing complex, multi-typed graph-structured data. However, their performance often varies across domains due to challenges such as handling diverse node and relation types, capturing dynamic temporal patterns, and providing model explainability. To address these limitations, this paper proposes the Dynamic Meta-Attention Heterogeneous Graph Transformer (DMAHGT) as a novel heterogeneous graph model that incorporates four key innovations: a two-level hierarchical attention mechanism for nuanced message passing; dynamic meta-attention that adapts weights based on node features, relation types, and temporal information; meta-learning capabilities for cross-dataset adaptation via a CrossDatasetMetaLearner; and inherent explainability through importance scoring of nodes and features. The proposed model demonstrates robustness and scalability, particularly for real-world applications involving complex and evolving graph structures, such as academic and social networks. Its ability to dynamically adapt to different relational semantics and temporal contexts highlights its potential as a versatile solution for heterogeneous graph-based learning tasks. The proposed model is evaluated on three benchmark datasets—ACM, IMDB, and DBLP—and is comprehensively compared with several state-of-the-art heterogeneous graph models, including HAN, HGT, HGCN, HeteroGNN, IGNN, and the meta-learning baseline MAML. The experimental results demonstrate that DMAHGT consistently outperforms these baseline approaches, achieving superior performance in node classification, clustering, and similarity search tasks.