Recent Advances in Big Data Analytics and Artificial Intelligence: A Comprehensive Review

Abstract

The rapid growth of data generated from digital platforms, sensors, and interconnected systems has led to the emergence of Big Data as a critical resource for intelligent decision-making. At the same time, Artificial Intelligence (AI) has evolved significantly, offering powerful techniques to extract meaningful insights from large-scale and complex datasets. This paper presents a comprehensive review of recent advances in Big Data analytics and Artificial Intelligence, focusing on their integration, methodologies, and practical applications. Key Big Data frameworks and analytics paradigms are discussed alongside state-of-the-art AI techniques, including machine learning, deep learning, and natural language processing. The review highlights recent trends such as real-time analytics, scalable AI models, and generative intelligence, while also examining major challenges related to data quality, privacy, scalability, and model interpretability. Furthermore, emerging research directions, including explainable AI, edge–cloud intelligence, and sustainable AI systems, are explored. This review aims to provide researchers and practitioners with a consolidated understanding of current developments and future opportunities at the intersection of Big Data analytics and Artificial Intelligence.

Introduction

The text presents a comprehensive review of the integration of Big Data analytics and Artificial Intelligence (AI), highlighting how their convergence enables intelligent, scalable, and data-driven decision-making across multiple domains. The rapid growth of data from sources such as social media, IoT devices, cloud platforms, and enterprise systems has exceeded the capabilities of traditional analytics, making advanced Big Data and AI techniques essential.

Big Data analytics is characterized by the 5Vs—volume, velocity, variety, veracity, and value—and relies on distributed frameworks like Hadoop, Spark, Flink, Storm, and Kafka to process large, heterogeneous datasets. When combined with AI, Big Data analytics supports descriptive, predictive, and prescriptive decision-making.

Key AI techniques applied to Big Data include machine learning for classification and prediction, deep learning for analyzing unstructured data such as images and text, and natural language processing and computer vision for large-scale text and visual analytics. Recent advances include scalable and distributed AI models, real-time and streaming analytics, and generative AI and foundation models, which significantly enhance analytical capabilities.

The integration of Big Data and AI has enabled impactful applications in healthcare (diagnosis and patient monitoring), finance (fraud detection and risk analysis), and smart cities and industry (traffic management, predictive maintenance, and resource optimization).

Despite these advancements, challenges remain, including data quality issues, high computational costs, privacy and security risks, and lack of model interpretability. The paper emphasizes the need for future research in explainable and trustworthy AI, edge–cloud intelligence, energy-efficient (green) AI, and the integration of AI with emerging technologies such as blockchain and digital twins. Overall, the review serves as a consolidated reference on current progress, challenges, and future directions in Big Data analytics and AI.

Conclusion

This paper presented a comprehensive review of recent advances in Big Data analytics and Artificial Intelligence, highlighting key techniques, emerging trends, applications, challenges, and future research directions. The synergy between Big Data and AI has enabled intelligent data-driven solutions across diverse domains, including healthcare, finance, and smart cities. Despite notable progress, challenges related to data quality, scalability, privacy, and model interpretability remain open. Addressing these challenges is essential for realizing the full potential of AI-driven Big Data analytics. Overall, this review provides valuable insights for researchers and practitioners and serves as a foundation for future advancements in this rapidly evolving field.

References

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Copyright

Copyright © 2026 Bharat Shelke, Prajkta More. 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.