The challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-The-Art outcomes in all aspects.