Cloud forensic tools are essential for automating the collection, preservation, and analysis of digital evidence in cloud environments, ensuring efficiency, accuracy, and legal compliance. Research in this area focuses on designing intelligent, scalable, and interoperable tools that can handle heterogeneous cloud infrastructures and multi-tenant architectures. Key topics include automated evidence acquisition from virtual machines, containers, and cloud storage, secure log collection and analysis, and forensic analysis of cloud-based applications and services. Other significant areas involve integrating machine learning and AI for anomaly detection, evidence classification, and correlation, as well as blockchain-based mechanisms for ensuring data integrity and provenance. Emerging research directions explore lightweight forensic agents for resource-constrained environments, privacy-preserving forensic toolkits, cross-platform interoperability, and open-source frameworks to facilitate reproducibility and transparency. Additionally, benchmarking, validation, and standardization of cloud forensic tools remain critical for enhancing the reliability and effectiveness of forensic investigations in complex cloud ecosystems.