Author(s)

Zice Gao ¹

Affiliation(s)

¹ University of Rochester, 500 Joseph C. Wilson Blvd., Rochester, NY, 14627, USA

Corresponding Author

Zice Gao

ABSTRACT

The fusion of Artificial Intelligence (AI) and Big Data has become essential for extracting valuable insights from complex and large-scale datasets. This paper reviews key architectural designs, core algorithms, and implementation pathways that enable efficient integration of AI and Big Data technologies. We discuss data fusion techniques, scalable machine learning models, multimodal learning frameworks, and privacy-preserving methods such as federated learning. Furthermore, the paper addresses practical challenges including system scalability, data heterogeneity, privacy concerns, and computational resource demands. Future directions focus on automation, green computing, and explainable AI to enhance transparency and sustainability. The fusion of AI and Big Data is poised to revolutionize various industries by enabling smarter decision-making and driving innovation.

KEYWORDS

Artificial Intelligence; Big Data; Data Fusion; Distributed Machine Learning; Multimodal Learning

CITE THIS PAPER

Zice Gao. Fusion Strategies of Artificial Intelligence and Big Data: Architecture, Algorithms, and Implementation Pathways. Journal of Artificial Intelligence Practice (2026). Vol. 9, No.1, 33-38. DOI: http://dx.doi.org/10.23977/jaip.2026.090105.

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