Trends, Challenges, and Future Directions in AI-Driven Big Data Technologies: A Review

The rapid growth of data generated from digital platforms, sensors, and connected devices has accelerated the integration of Artificial Intelligence (AI) with Big Data technologies. AI-driven Big Data systems enable advanced data processing, predictive analytics, and intelligent decision-making across diverse application domains. This review paper provides a comprehensive overview of the current trends, key challenges, and future research directions in AI-driven Big Data technologies. It discusses emerging trends such as automated analytics, deep learning–based data modeling, real-time processing, and the convergence of AI with cloud, edge, and Internet of Things (IoT) environments. The paper also highlights critical challenges, including data privacy and security concerns, scalability issues, high computational costs, and data quality and bias. Furthermore, it explores promising future directions, such as explainable and trustworthy AI, hybrid intelligence models, and sustainable AI-driven data ecosystems. This review aims to offer valuable insights for researchers and practitioners by summarizing recent advancements and identifying open research opportunities in the evolving landscape of AI-driven Big Data technologies