This comprehensiv e literature survey investigates the evolving landscape of Anomaly Object Recognition in Surveillance Videos, focusing on advancements and methodologies proposed in recent research. In the realm of video surveillance, the escalating volume of visual data n ecessitates robust systems capable of autonomously detecting unusual events. The survey encompasses a diverse array of approaches, leveraging artificial intelligence, machine learning, and computer vision, with applications spanning public safety, security , and critical infrastructure The survey begins by outlining the critical importance of anomaly object recognition in automating the identification of deviations from expected patterns, addressing the limitations of traditional manual monitorin g methods. Key components of anomaly object recognition systems, including learning normal patterns, real time detection, alert generation, adaptability, and integration with surveillance infrastructure, are highlighted for a comprehensive understanding. The literature survey subsequently categorizes and analyzes a plethora of research papers, each contributing unique perspectives to the field. The methodologies explored encompass high dimensional classification modeling, optimization algorithms, generativ e networks, deep convolutional neural networks, and bidirectional consistency models. Applications range from public spaces and critical infrastructure to retail environments, traffic monitoring, and industrial security.