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In the dynamic landscape of Intelligent Transportation Systems, this research pioneers strategies for efficient route prediction, particularly vital for emergency vehicles (EVs). The HL-CTP model employs incremental learning, enhancing accuracy by fine-tuning predictions based on historical data. Complementing this, the SG-TSE model adjusts traffic lights, minimizing the negative impact of congestion on both regular traffic and EV preemption. Recognizing the limitations of traditional machine learning in Internet of Vehicles networks, our third objective utilizes YOLOv4-based traffic monitoring, incorporating the Kalman filter for real-time IoV environment modeling. Policymakers can leverage this data for informed decisions, improving transportation efficiency, reducing congestion, and enhancing safety. Integrating RSUs efficiently manages network resources, contributes to smarter transportation systems, and elevates urban living standards. In conclusion, this research not only advances route prediction and EV preemption but also adds value to the broader landscape of intelligent and responsive transportation systems, benefiting society at large.