Evidence acquisition in cloud forensics focuses on reliably identifying, collecting, and preserving digital evidence from distributed, virtualized, and multi-tenant cloud environments while maintaining integrity and legal admissibility. Research in this area emphasizes the development of automated and scalable acquisition frameworks capable of handling diverse cloud services, storage platforms, and virtual machines. Key topics include secure and forensically sound data collection from cloud storage and applications, recovery of deleted or volatile data, and acquisition from multi-cloud or hybrid cloud infrastructures. Other important areas involve the use of blockchain for tamper-proof evidence tracking, encryption-based acquisition methods for privacy protection, and real-time monitoring for proactive evidence collection. Emerging directions also explore AI- and ML-assisted prioritization of evidence, acquisition from containerized and serverless architectures, standardized procedures for cross-jurisdictional investigations, and integration of cloud forensic readiness models to reduce delays in incident response. Addressing challenges of dynamic data, large-scale environments, and multi-tenant scenarios remains central to advancing cloud evidence acquisition research.