Abstract: As high-speed data streams from financial transaction systems, cloud infrastructures, Internet of Things devices, and huge communication networks undergo rapid change, real-time analytics are becoming increasingly important. The incapacity of traditional batch-oriented and rule-based anomaly detection systems to handle a range of data distributions, low latency requirements, and a constant stream of new data is rendering them obsolete. This study provides a thorough AI-driven analytics pipeline aimed at discovering anomalies in real time in quickly flowing data streams in order to address these problems.

Real-time data reprocessing, adaptive feature engineering, complex artificial intelligence models, and techniques for handling growing streams are all included in the proposed pipeline, which is a complete system. Deep learning techniques like autoencoder-based models and recurrent neural networks, along with statistical detectors, can effectively identify changes in streaming data across both brief and extended durations. This project's main goal is to improve online learning by teaching individuals how to combat notion drift. These enable the pipeline to quickly adjust to new data patterns without affecting the day-to-day operations of the company.

The architecture relies on contemporary distributed stream processing platforms and containerized deployment techniques to guarantee scalability, fault tolerance, and speed of operation. The suggested methodology beats current anomaly detection systems in terms of detection accuracy, robustness, and sub-second reaction times, according to thorough experimental evaluation on both synthetic and real-world streaming datasets. Overall, our work gives a valuable and adaptable foundation for using AI-driven anomaly detection approaches in real-time data stream applications that are vital to the mission.

Keywords: AI-driven pipelines, stream processing, online learning, idea drift, deep learning, autoencoders, distributed systems, real-time analytics, fast data streams, and abnormality detection are some of the most important terms.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14111