Effective crowd management in dynamic urban environments is critical for public safety, efficient mobility, and enhanced citizen experiences. Traditional methods, such as static planning and manual interventions, often fail to adapt to the real-time and unpredictable nature of human behavior during large-scale events or emergencies. This chapter proposes an innovative framework for autonomous crowd management that integrates Reinforcement Learning, Digital Twins, Multi-Agent Systems, and Intelligent Internet of Things devices. Our approach leverages RL for adaptive decision-making, MAS for decentralized collaboration, and DTs to create virtual replicas of urban environments for real-time monitoring and simulation. The integration of multimodal IoT data streams enhances situational awareness and enables dynamic system responses to evolving crowd conditions. By prioritizing human-in-the-loop mechanisms, the proposed framework ensures transparency, ethical oversight, and trust in automated decisions. This work demonstrates a dynamic, scalable, and proactive crowd management system that not only addresses immediate operational challenges but also provides a roadmap for future research in smart urban environments.