The convergence of the Internet of Things (IoT) and Computer Vision (CV) technologies is revolutionizing public safety and Quality of Life (QoL) in smart cities. This chapter explores how data-driven event detection and dispatch management powered by Artificial Intelligence (AI) and optimization can transform public safety. Moving beyond traditional methods like manual reporting, we propose a data-driven approach utilizing IoT sensors (i.e. optical cameras) to capture real-time data. This data feeds into advanced AI models capable of identifying and anticipating potential events, ranging from security threat detection to traffic management. Upon event detection, a robust dispatch management system integrated with AI utilizes optimization algorithms to efficiently allocate resources and route first responders. An example of a specific Automated Event Detection and Dispatch Management System (AEDDMS), known as Remote Area Management Systems (ReAMS), is presented to demonstrate the concept. ReAMS employs AI to monitor remote areas for security threats. While focusing on this example, the chapter emphasizes the broader applicability of this approach to various smart city domains. The chapter also acknowledges the challenges, future work, and societal considerations like infrastructure planning, safety, ethics, and privacy surrounding these smart city solutions. This chapter paves the way for a data and AI-driven future where technology empowers us to build safer, more resilient communities.