This paper presents an in-depth review of deep learning methods employed in the field of cloud forensics analysis, highlighting current advancements, challenges, and future directions. With the rapid adoption of cloud computing, forensic investigations face unique challenges, particularly in data collection, evidence analysis, and maintaining chain of custody. This survey explores how deep learning enhances cloud forensic processes, improving accuracy, automation, and scalability. Key areas such as malware detection, anomaly detection, log analysis, and privacy-preserving techniques are discussed. The paper also examines the gaps in existing research and suggests areas for future exploration.