It is beneficial to encapsulate in a context age-friendly communities sustainable and affordable innovative solutions and their benefits for a comfortable, meaningful, and independent life in an intelligent environment. Assessing and supervising older adults' services and facilities are essential to improving well-being. These assessments form the basis for creating an age-friendly urban environment based on the IoT's smart integration. Our research focuses on developing a frame for services selection that needs to be optimized by finding a pattern to identify User Preferences (UPs). The deployment of Markov's chain is followed by the Quality of Service (QoS) for the available services in an urban ecosystem. We then utilized the Best Worst Method (BWM), a Multi-Criteria Decision Making (MCDM) method, to rank the urban locations. We also prove that the proposed method outperforms the Analytic Hierarchy Process (AHP). The proposed methodology provides a prioritized list of the age-friendly places in an EU city based on the provision of the services considering the UPs. Validation has been performed through a case study using accurate QoS performance data by using the Place Standard tool.