Emergency transport systems are essential for delivering prompt assistance during critical situations such as medical emergencies, natural disasters, and accidents. Their efficiency relies on several key factors, including rapid response times, effective resource allocation, and robust coordination among emergency units. This research aims to optimize emergency transport operations using advanced modeling techniques, particularly through multiagent systems (MAS), which simulate and analyze dynamic urban environments. By employing multiagent systems, the study models the complex interactions between emergency vehicles, traffic control systems, and response personnel. These agents are designed to adaptively respond to real-time conditions such as traffic congestion, road blockages, and simultaneous emergencies. A strong emphasis is placed on optimizing routing strategies to minimize delays and maximize coverage areas. The findings from this research provide valuable insights for urban decision makers, empowering them to develop systems that can effectively adapt to the challenges of modern urban landscapes, ultimately ensuring optimal outcomes in critical situations.