Developments in artificial intelligence (AI) within the last decade or so include the introduction of context-aware models which improve the effectiveness of data informed decisions. Unlike traditional AI systems which are static and depend on data, context-aware AI is responsive and adjusts outputs according to user and time, making decision-making more precise, flexible, and real time. This integrated context-aware artificial intelligence system developed for data engineering works toward resolving the problem of ineffective contextualized models. The system combines context elements with data pipelines which increases efficiency and adaptability across various domains. Its performance as compared to conventional AI processes was assessed by extensive research done on the Movielens, Bank Marketing, and NYC Taxi Trip Duration datasets which provided: 82% accuracy in automated recommendation generation, 20% decrease in inaccuracies for taxi trip duration estimate, and 91% accuracy in predictive marketing models which is a significant value. The results confirm the scalability and domain adaptability of the proposed framework. Nonetheless, real-time computing cost persists as a concern, as the processing of high-dimensional contextual data elevates latency. Moreover, although the framework is versatile across various industries, it frequently necessitates domain-specific customization, hence constraining its direct plug-and-play functionality. The next phase of the research intends to: Enable more effective real-time distributed data processing, foster trust and transparency via XAI techniques, and improve data privacy and contextual adaptability with federated learning approaches. This work promotes the development of AI-based decision-making systems for business intelligence, automation, and complex systems integration through automatic real-time context-aware AI design interpretability and effectiveness optimization.