International Journal for Recent Development in Science and Technology

In emergency situations, hospitals are often overwhelmed with patients, leading to full occupancy of beds and critical resources such as ventilators, ICUs, and oxygen supply, which in turn results in the collapse of healthcare systems.Ambulance drivers face significant delays in transferring patients due to the unavailability of necessary medical resources, often waiting outside hospitals for extended periods.Unfortunately, this lack of coordination and communication results in the loss of many lives.One of the key challenges is the absence of a unified system that connects nearby hospitals to monitor the patient's condition inside the ambulance in real-time.Additionally, the hospitals with the required resources, such as ICU beds or ventilators, are not identified promptly, exacerbating the situation.To address these issues, this paper proposes a comprehensive system that integrates patient monitoring with real-time hospital data.The proposed system uses artificial neural networks (ANN) to analyze the patient's condition and predict the level of care required, facilitating decision-making before reaching the hospital.Simultaneously, a centralized server aggregates data from nearby hospitals, providing a real-time overview of available medical resources, such as vacant ICU beds, ventilators, and oxygen supplies.By connecting ambulance services with this centralized server, hospitals with the necessary equipment can be identified and patients can be directed there, reducing delays and ensuring timely medical intervention.This system not only aims to enhance the decision-making process but also ensures that emergency patients are swiftly directed to hospitals with adequate resources, reducing the risk of critical delays.The seamless integration of hospital resources and patient data could ultimately save lives, particularly in times of medical crises when every second counts.By improving the coordination between ambulances and healthcare facilities, the system will streamline the patient transfer