<p>The perception of safety plays a crucial role in fostering sustainable urban communities, as it influences the willingness of citizens to engage in community activities and their residential choices. Understanding the factors associated with safety perception is, therefore, critical for community development. While previous studies focus on static built environmental features, this research leverages artificial intelligence (AI) to examine how street-level waste management shapes safety perceptions in metropolitan areas. Through computer vision and machine learning approaches, we quantify safety perception levels and identify various types of street-level waste in New York City. Our analysis reveals a strong negative relationship between uncontrolled waste (particularly widespread litter) and perceived safety, while properly managed waste shows weaker associations with safety perception. These findings demonstrate that dynamic environmental management factors, rather than just static infrastructure, are critically associated with urban safety perceptions. The study advances both theoretical understanding and practical strategies for enhancing urban safety perception through improved waste management services.</p>