Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios.