Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. To address the sparse anomaly supervision dilemma, we propose a hybrid learning objective that integrates dual-stream reconstruction loss with prototype-based contrastive discrimination, enabling the joint optimization of pattern restoration and discriminative representation learning. Our extensive experiments on the UCF-Crime, UBI-Fights, and UBnormal datasets demonstrate state-of-the-art performance, achieving AUC scores of 98.9%, 94.7%, and 82.9%, respectively. The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. Our optimized window-shift attention mechanism also reduces computational complexity, making MT-CMVAD a robust and efficient solution for safety-critical video understanding tasks.