An innovative software-driven solution is designed to optimize traffic flow and expedite the transit of emergency vehicles through congested urban areas. The system leverages advanced machine learning models for both audio and image processing to dynamically adjust traffic signals and prioritize the swift passage of emergency vehicles. The audio processing module utilizes a 1D convolutional neural network (1DCNN) and the Librosa library to detect ambulance sirens in real-time audio data, triggering the activation of the visual processing module. The visual module, powered by the YOLOv5 object detection framework, identifies ambulances in live video feeds, prompting immediate adjustments to traffic signals. Extensive testing demonstrates the system’s efficacy in prioritizing emergency vehicles, with key metrics including signal transition times, ambulance detection accuracy, and overall traffic flow improvement. The holistic integration of audio and visual processing and dynamic traffic signal adjustment positions this solution as a robust and adaptable approach to addressing challenges in emergency vehicle navigation within urban traffic scenarios.