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The rise of cloud computing has revolutionized data storage, enabling organizations to offload data management tasks to third-party cloud service providers (CSPs) with the promise of scalability, cost efficiency, and accessibility. However, this paradigm shift introduces significant privacy and security concerns, particularly when sensitive data is stored and processed on shared or remote infrastructure. In response to these challenges, a range of privacy-preserving data storage techniques have been developed to secure cloud-based databases against unauthorized access, data leakage, and malicious insider threats.This research paper provides a comprehensive analysis of the primary techniques used to preserve data privacy in cloud databases. These include various forms of encryption (such as homomorphic and order-preserving encryption), data fragmentation and hybrid cloud storage, anonymization methods, secure indexing, and emerging blockchain-based solutions. The paper examines practical implementations through case studies, including Microsoft Azure, Google Cloud, IBM Cloud, and blockchain-based systems such as MedRec.