Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists.