While traditional multimedia systems focused on efficient coding and storage of media types and their temporal relationships, the current demand for rich and customized experiences calls for a deeper understanding of semantic content. In this study, we propose the integration of semantic-level processing into multimedia systems, enriching content with information about real-world entities, such as objects, actions, agents, and language interpretation. The main contribution of this dissertation is the proposal of an architecture for real-time multimedia data enhancement that is able to use machine learning techniques to extract semantic representations and incorporating it into multimedia data streams as a native and basic service. To provide a concrete demonstration of the proposal, we implement two use cases that serve as proofs-of-concept, showing the feasibility of the architecture and showcasing its effectiveness in practical scenarios.