(untitled) (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2024)
The accelerated growth of generative AI has transformed the relationship between Big Data Analytics (BDA) and Machine Learning (ML), enabling intelligent data-driven systems with unprecedented scalability, automation, and representation learning capability. This paper presents a comprehensive review of how BDA pipelines integrate with classical machine learning, deep learning, and modern generative AI systems such as Generative Adversarial Networks (GANs) and large language models (LLMs). A detailed examination of system modules—including data ingestion, distributed storage, feature engineering, model training, generative augmentation, and deployment—is presented to understand their role in modern analytics ecosystems. Although the paper discusses generative AI trends, all literature references are restricted to work published before December 2023. The proposed system architecture demonstrates how organizations can combine big data infrastructure with generative AI-driven ML pipelines to enhance decision-making, synthetic data generation, automation, and enterprise intelligence.