Big Data Analytics Using Artificial Intelligence: A Review Of Platforms, Learning Techniques And Applications

Organizations in healthcare, banking, education, retail and government generate data in volumes, varieties and speeds that traditional database tools cannot process effectively. Big Data Analytics using Artificial Intelligence (AI) combines scalable data platforms with machine learning and deep learning to identify patterns, predict future trends and generate actionable insights from this data. This paper reviews the field from both the systems and the learning perspective. It explains the characteristics of big data, the evolution of processing frameworks from MapReduce and the Hadoop distributed file system to in-memory engines such as Apache Spark, stream processors such as Apache Kafka and Flink, and the lakehouse model. It then examines how machine learning and deep learning are scaled through distributed libraries and frameworks, and how analytics serves applications such as disease prediction, fraud detection, recommendation systems and learning analytics. A five-layer architecture comprising data sources, ingestion, storage, processing and AI, and visualization is proposed, followed by a research gap analysis, objectives, a comparison of frameworks and a discussion of challenges including data quality, privacy, cost and the hidden maintenance burden of machine-learning systems. The review concludes that AI-driven big data analytics converts raw data into timely, meaningful information for better decision-making.