However, these systems still face significant challenges related to data security and privacy in IoT networks, as well as limitations in accuracy, scalability, and adaptability to effectively address diverse incidents and mitigate false alarms [17]. Therefore, a comprehensive study is needed to systematically organize and evaluate information regarding the implementation of IoT and deep learning-integrated systems for public security management. The lack of dedicated research that systematically compiles and organizes the literature on the application of deep learning and IoT in urban security monitoring and management limits access to prior experiences that could inform the potential of these systems in addressing rising crime rates in complex environments. While existing studies explore IoT and AI in general terms, few specifically focus on how deep learning can optimize the accuracy of crime pattern detection and incident prediction. To bridge this gap, this study seeks to answer the following research question: How can deep learning strengthen public security and safeguard data in smart cities? This question is addressed through five sub-questions aimed at analyzing how intelligent systems integrating IoT and deep learning have been effective in managing violence and crime in urban settings. Thus, this study aims to examine existing implementations of deep learning models applied to violence and crime monitoring systems in IoT-based smart cities, following the guidelines established by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [18] and the methodology proposed by [PERSON] and [PERSON] [19]. These methodological approaches--widely employed in systematic reviews within the field of Computer Science--were used to design and implement a scientific search protocol across databases, including Scopus and Web of Science. This protocol enabled the identification of relevant models and architectures for urban security monitoring systems based on IoT technologies and artificial neural networks. Furthermore, a structured scientific protocol was developed to guide this study, ensuring the systematic organization and prioritization of the gathered information. Finally,a qualitative analysis of 45 reviewed studies was conducted to examine the scope and limitations of deep learning models for security monitoring in smart cities, considering technical, ethical, and privacy-related aspects affecting citizens using these systems. This analysis provides a comprehensive perspective on the applicability of these technologies within contemporary urban contexts. This research is essential for developers, researchers, educators, students, and institutions such as regional governments [20], as it provides critical insights for enhancing security in high-risk and moderate-risk zones through deep learning applications. Additionally, findings from previous studies in this field will serve as a valuable reference for public authorities responsible for citizen safety, offering a foundation for urban planning and the implementation of security systems tailored to specific risk levels. This approach will facilitate the design of customized security strategies, improving protection and operational efficiency across different urban sectors based on empirical evidence. This paper is structured as follows: Section 2 describes the methodology employed, including a detailed explanation of the scientific search protocol designed and applied. Section 3 presents the results, organizing findings according to the research questions outlined in the methodology. Section 4 discusses the implications of these findings, analyzing their impact on current knowledge and proposing practical applications. Finally, Section 5 presents the conclusions, summarizing key outcomes and suggesting directions for future research.