Real-time patient monitoring using deep learning has emerged as a transformative approach in medical diagnosis, providing healthcare professionals with timely and accurate information for early detection and intervention. This paper explores the integration of deep learning algorithms with real-time patient monitoring systems, leveraging diverse physiological signals such as vital signs, electrocardiograms (ECG), blood pressure, and temperature. To collect basic physiological data, the audit uses a grouping of Internet of Things (IoT) contraptions, for instance, the MLX90614 non-contact infrared inner intensity level sensor, the ECG sensor module, and the beat oximeter. Using the MQTT show, the collected data is sent off a server where a convolutional neural network (CNN) with an attention layer that has been trained will analyze it.