The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received increased research attention. Several abnormal behaviour detection systems have been proposed over the years to ensure human safety. However, only a few comprehensive and systematic reviews report on the current state and future direction of video anomaly detection(VAD) research. This present effort aims to contribute a systematic and detailed review of current research and advances in the detection of anomalous actions and entities in videos. The review focuses on studies published between 2003 and 2023. During the literature selection process, 530 scholarly articles were identified and evaluated to showcase prevalent research trends, techniques, datasets, and frameworks within the realm of VAD This review aims to offer a comprehensive understanding of key areas of focus among researchers, provide resources for commonly used public datasets for evaluation and experimentation, and examine advancements and integration of network design to accommodate the needs for handling multimedia information. To sum up, this study highlights various potential opportunities and obstacles about the VAD domain.VAD has gained significant interest in recent years due to its potential applications in various domains such as security, surveillance, traffic monitoring, medical imaging among others. Areas of further research include, Anomaly detection in real-time, multi-camera anomaly detection, privacy preserving in VAD to mention a few. The study revealed some key trends and challenges in VAD that can guide future research direction. © 2024 Elsevier B.V.