Data privacy and security have become paramount concerns in the realm of artificial intelligence (AI) due to the increasing reliance on vast datasets for training AI models. This review paper explores the potential of blockchain technology to enhance data privacy and security in AI applications. Blockchain, known for its core features of decentralization, immutability, transparency, and security, offers a promising framework to address data privacy challenges. Keywords like decentralized data storage, access control mechanisms, data provenance, and privacy-preserving machine learning are discussed in the context of blockchain integration with AI. Several use cases, including healthcare, finance, supply chain, and identity verification, demonstrate the practical applicability of blockchain in safeguarding sensitive data. However, challenges related to scalability, regulation, and adoption must be addressed. The paper concludes by highlighting emerging trends, research directions, and the importance of ongoing efforts to harness blockchain`s potential for preserving data privacy in AI.

Blockchain, Data Privacy, Artificial Intelligence, Decentralized Data Storage, Access Control, Data Provenance, Privacy-Preserving Machine Learning, Use Cases, Challenges, Emerging Trends.

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