Video Anomaly Detection (VAD) is the process of identifying anomalous events by analyzing spatiotemporal patterns in video. Furthermore, VAD is a complex task due to difficulties in obtaining frame-level annotations, data imbalance issues, and the equivocal and context-dependent nature of video anomalies. To address these issues, this article presents a weakly supervised learning-based Multi-head Global-Local Attention-enabled Dual-Stream Network (MGLA-DSNet) that effectively utilizes spatial (appearance) and temporal (motion) features, with an emphasis on context dependency. The proposed model uses two streams to extract RGB and optical flow features corresponding to appearance (spatial) and motion (temporal) properties, respectively. Subsequently, multi-head global and location attention with adaptive gating and head-wise specialization is applied to the concatenated RGB and Flow features to efficiently model global and local contexts, respectively, using multiple instance learning Finally, the proposed MGLA-DSNet model outperforms state-of-the-art methods across three benchmark datasets, including CUHK Avenue, ShanghaiTech Campus, and UCF-Crime.