Artificial Intelligence and Big Data: A Comprehensive Review

Abstract

The proliferation of large-scale, heterogeneous, and high-velocity data necessitates the integration of Artificial Intelligence (AI) techniques with big data analytics for efficient knowledge discovery and intelligent decision-making. This paper presents a systematic technical review of AI-based big data analytics, synthesizing recently published peer-reviewed surveys and studies. The study analyzes how machine learning and deep learning models enhance big data processing through automated feature extraction, predictive modeling, and anomaly detection, while also identifying key limitations related to scalability, interpretability, data imbalance, and computational overhead. Application-centric insights are discussed across critical domains such as Industry 4.0, cyber security, decision support systems, and learning analytics, emphasizing real-time processing, distributed architectures, and privacy-preserving mechanisms.

Furthermore, this paper highlights fundamental and algorithmic challenges, including streaming data processing, multimodal learning, adversarial robustness, and energy-efficient deployment in distributed and edge computing environments. Emerging trends towards AI 2.0 are examined, focusing on knowledge-based learning, neuro-symbolic integration, and explainable AI frameworks. In this review, the open research challenges and future directions for developing scalable, transparent, and secure AI-enabled big data systems are identified, while providing a unified technical reference for researchers and practitioners.