The rapid growth of cloud computing and big data analytics has intensified concerns over privacy when sensitive data are outsourced to third-party cloud providers. Traditional encryption techniques protect data confidentiality but significantly limit the ability to perform expressive and efficient queries, particularly in distributed and multi-cloud environments. Motivated by the increasing demand for secure analytics across healthcare, finance, IoT, and collaborative cloud platforms, this review systematically examines privacy-preserving query processing techniques for encrypted data in multi-cloud settings. Following PRISMA guidelines, a systematic literature review of published peer-reviewed studies is conducted. The reviewed approaches are categorized into homomorphic encryption-based methods, searchable encryption techniques, secure multi-party computation, trusted execution environments, and hybrid architectures. The analysis highlights key trade-offs among privacy guarantees, query expressiveness, computational efficiency, and scalability. While hybrid and multi-cloud approaches improve flexibility and fault tolerance, they introduce new challenges related to leakage, communication overhead, and trust assumptions. This review identifies critical research gaps, including limited real-time support, side-channel vulnerabilities, and the absence of standardized benchmarks. Finally, future research directions are outlined, emphasizing AI-assisted encrypted querying, federated analytics, and post-quantum privacy-preserving frameworks for multi-cloud environments.