Real-time decision-making is a strength for industries where decision-making in the fast-changing environment has influenced decision-making due to advancing technologies. The current paper outlines a conceptual framework that leverages data science to improve real-time decision-making in flexible and expandable systems. The architecture combines machine learning and streaming analytics for high-velocity data streams, which provide business decisions in milliseconds. As a distributed cloud-based system, the proposed system guarantees high scalability and flexibility since the predictive models are updated in real time to adjust to the new data pattern. The proposed framework outperforms other multiple models by using the decision tree, deep learning, and ensembling; accuracy, speed, and resources results are more persuasive. Actual implementations substantiate that the system’s dynamic aspects increase the accuracy of the premeditated choices by 25% when compared to the normal unelastic models and, at the same time, slashes the time delay by 40%. The result of this research gives real-life insight into the effective integration of real-time data science into BI systems, making it perfect for building strong BI systems in the future. The paper’s results enrich the current discussion on real-time analytics by presenting a clear guideline for building larger-scale decision-making frameworks for domains that include finance, healthcare, and IoT-based industries.