Efficient emergency response is a critical component of public safety, where even small reductions in travel time can have a profound impact on outcomes. Traditional navigation systems commonly used in non-emergency scenarios prioritize traffic congestion and route length to determine the fastest path. However, these systems are often suboptimal for emergency vehicles, which need to maintain the highest possible average speed rather than simply avoiding congested areas. This paper introduces a speed limit-based path prediction model designed specifically for emergency routing, where road segments with higher speed limits are favored to minimize travel time. Unlike congestion-based models, which primarily focus on real-time traffic flow, the proposed model evaluates the potential for higher average speed on roads with elevated speed limits, even if they are longer in distance. Through extensive simulations across various urban and suburban environments, we found that the speed limit-based model can reduce emergency vehicle travel times by up to 20%, especially in highly congested areas. Additionally, this approach proved more effective in maintaining consistent speeds in scenarios where traditional models would suggest slower, congested routes. The study also highlights the importance of integrating real-time road conditions, such as traffic signal timing and road closures, to further enhance the model’s predictive accuracy. Our work implemented speed limit-based routing and it can serve as tool for emergency management, improving earlier traffic systems to improve response times and save lives.