The task of detecting violence is crucial and has significant repercussions for both public safety and societal well-being. Because real-world settings are dynamic, traditional methods frequently find it difficult to adjust, which has led to the investigation of new computational techniques. This study examines modern approaches to violence detection by utilizing knowledge from multidisciplinary and artificial intelligence research. By combining computer vision, signal processing, and behavioral psychology, we offer a comprehensive framework that can be used to recognize and classify violent incidents in a variety of settings. We investigate the effectiveness of cutting-edge methods, such Long-Short-Term Memory (LSTM) networks, in identifying minor behavioral indicators that suggest aggression and capturing temporal correlations using real-world datasets.