Abstract
The rising integration of emergency healthcare services with the Internet of Medical Things (IoMT) creates a significant opportunity to improve real-time communication between patients and emergency vehicles like ambulances. Fast and reliable data interchange is crucial in an emergency, especially for those with chronic conditions who rely on wearable IoMT devices to monitor vital health signs. However, establishing consistent communication in real-world conditions such as restricted signal strength, changing distances, and power constraints remains a major difficulty. This paper provides an intelligent communication framework that uses a one-dimensional deep convolutional neural network (1D-CNN) and Lagrange optimization techniques to improve energy efficiency and data transmission speeds. Unlike many earlier models, our technique takes into consideration real-world characteristics such as signal-to-interference-plus-noise ratio (SINR), transmission power, and the distance between the ambulance and the patient's device. The primary goal is to identify the ideal communication distance for dependable, energy-efficient data transfer during urgent emergency situations. The findings show that the suggested system enhances communication reliability, consumes less energy, and increases the possible data rate. This framework accelerates, smartens, and strengthens emergency healthcare communication systems by combining deep learning and mathematical optimization. These findings contribute to the progress of intelligent healthcare infrastructure, opening the way for responsive and dependable emergency services that can adapt to changing conditions while maintaining high performance and patient safety.