This paper explores the potential of genetic algorithms (GAs) in optimising emergency vehicle response times through both dynamic routing and adaptive traffic signal control. Traditional deterministic routing methods, such as Dijkstra’s and A*, fail to account for real-time traffic fluctuations or signal coordination, often leading to delays that reduce patient survival rates. A review of existing studies demonstrates that GAs outperform static algorithms by dynamically re-evaluating routes, optimising multi-stop journeys, and scaling to fleet-level management. Similarly, GAs have shown effectiveness in adjusting signal timings at intersections to minimise delays under fluctuating traffic volumes. However, most research addresses routing and signal optimisation separately, leaving a gap in integrated systems that combine both strategies. This paper highlights the need for GA-based frameworks capable of jointly coordinating emergency vehicle routing and signal pre-emption, tested on realistic urban networks. Such integration could significantly enhance emergency response efficiency and provide a scalable, adaptable solution for real-world applications.