The novel context-aware machine learning is based on state-of-the-art real-time data engineering processes that operate in shifting entity correlations. To this end, this paper presents a new architecture that combines knowledge graph construction with real-time stream processing to underpin the machine learning flow in a context-aware manner. The proposed system uses graph neural networks (GNNs) for updates and embeddings in real-time for dynamic integration of contextual information into the other machine learning models. This makes the approach ideal as changes in the relations of entities can be captured almost in real time, and models remain valid. The effectiveness of the architecture can be illustrated by use cases related to customer profiling and equipment failure prognosis. In a consumer classification, one has to continually modify customer profiles as others come across the new interaction to work on effective targeting and the subsequent personalization improvement. Predictive maintenance stores changing information on equipment to predict future failure. These applications show a 40% improvement in model accuracy and take 50% less time than normal methods for feature engineering. This research bridges computer science, particularly graph theory, and real-world data engineering by demonstrating the value of knowledge graphs and GNNs within machine learning pipelines. By incorporating contextual features, the system provides a feasible and flexible solution for current data trends, allowing for further development of smarter and more sensitive ML systems. The study points out real-time context sensitiveness as central to the advancement of machine learning, a landmark discovery.