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

Abstract

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.

Introduction

The text reviews the role of Artificial Intelligence (AI) in enabling Big Data technologies to effectively process and analyze the rapidly growing volumes of data generated from digital platforms, IoT devices, and enterprise systems. Traditional data analytics methods are insufficient for handling the scale and complexity of Big Data, making AI essential for intelligent data understanding, prediction, and automated decision-making.

AI-driven Big Data systems combine machine learning and deep learning with distributed frameworks such as Hadoop and Apache Spark, along with cloud and edge computing, to achieve scalable, real-time, and efficient data analytics. These technologies support advanced tasks including pattern recognition, anomaly detection, forecasting, and recommendation systems across diverse domains.

Current trends highlight increased automation in analytics, widespread adoption of real-time and streaming data processing, and growing integration with cloud–edge architectures. AI-driven Big Data applications are expanding rapidly in areas such as smart cities, Industry 4.0, healthcare, and autonomous systems, where timely and intelligent decision-making is critical.

Despite these advances, major challenges remain, including data privacy and security risks, high computational and energy costs, data quality and bias, and the lack of model transparency and interpretability. These issues limit trust and adoption, especially in sensitive sectors.

Future research directions emphasize the development of explainable and trustworthy AI, hybrid AI models, privacy-preserving and federated learning, and energy-efficient (green) computing infrastructures. Strengthening the integration of AI with edge, fog, and cloud computing is also expected to support next-generation, real-time, and sustainable Big Data analytics systems.

Conclusion

This review paper examined the evolving landscape of AI-driven Big Data technologies by analyzing current trends, key challenges, and future research directions. The integration of Artificial Intelligence with Big Data analytics has significantly enhanced the ability to process large-scale, complex datasets and extract actionable insights across various domains. Emerging trends such as automated analytics, real-time data processing, and the convergence of AI with cloud and edge computing demonstrate the growing impact of intelligent data-driven systems. Despite these advancements, several challenges—including data privacy and security concerns, scalability issues, data quality limitations, and lack of model transparency—continue to restrict the full potential of AI-driven Big Data technologies. Addressing these challenges is essential to ensure reliable, ethical, and efficient deployment of AI-based analytics solutions. Future research is expected to focus on explainable and trustworthy AI, hybrid intelligence models, privacy-preserving learning techniques, and sustainable computing infrastructures. By overcoming existing limitations and exploring these directions, AI-driven Big Data technologies can play a crucial role in enabling smarter decision-making and supporting innovation in data-intensive applications.

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Copyright

Copyright © 2026 Bharat Shelke, Prajakta More, Madhuri Mutyal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.