In order to alleviate the contradiction between supply and demand of professional pharmacists, integrate medical resources, and ensure the safety of patients’ medication, the telemedicine diagnosis system has played a great role. Today, in this “Internet +” era, all walks of life have begun to integrate with Internet technology. The purpose of this article was to discuss the practical utility of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of Internet hospitals, for which this article conducted in-depth discussion. This article first introduced the basic concepts, development, and characteristics of machine learning and natural language processing algorithms in detail, and carefully studied and analyzed the development and culture of traditional offline medical diagnosis models. Based on machine learning and natural language processing algorithms, a remote mobile medical diagnosis is designed. By combining with the medical diagnosis system of traditional hospitals, a new type of remote mobile medical diagnosis system for Internet hospitals was designed and developed, and the combination of traditional medical industry and Internet technology was deeply studied. According to each functional requirement, the image module, heart rate measurement module, and user setting module are designed, respectively. Compared to traditional medical diagnosis systems, the accuracy of the remote mobile medical diagnosis system based on machine learning applied in Internet hospital diagnosis in this article reached 80% or even higher. At the same time, it was found through experiments that when the evolution number was 3, the maximum fit value and average fit value were the same, both of which were 0.6. This indicates that the system can accommodate more than 10,000 people at the same time, and patients can receive good treatment plans, with a very broad application prospect