BI systems have long been based on historical data analysis and relatively unchanging dashboards and hence could not help in the dynamic environment where the decisions are made using the data. With the help of the advanced machine learning models and integrated real-time data ingestion mechanisms, this paper proposes an AI-powered BI architecture that can be applied to more effectively make the contemporary decision-support systems more adaptive and intelligent. The suggested multi-layered architecture uses data ingestion intuitive instruments like the Apache Kafka, processing instruments like Apache Spark, and model instruments like the XGBoost, LSTM, and BERT, with respect to analytics and translating queries. An //algorithmic// flow is followed, including a step-by-step description starting with data acquisition all the way to the delivery of recommendations. Based on experimental results, the prediction error is also significantly reduced (by 34% and 35% minimum absolute error (MAE) and root mean squared error (RMSE) respectively), and the latency of the system is found to improve by an impressive 70% compared to the traditional BI tools. The system also exerts the good training-validation convergence and the good generalization upon the business scenarios. These findings confirm the possibility of combining AI with BI equipment to help real-time, interpretable, and scalable decision-making in different industries.