Latest research in Digital Evidence Identification and Acquisition in IoT focuses on developing specialized methodologies and tools to address the unique challenges of investigating IoT ecosystems. Studies highlight frameworks such as the Forensic-Based (FB) Framework, which combines secure evidence acquisition with autonomous intrusion detection and machine learning to improve reliability and accuracy. Researchers emphasize the need for standardized processes to identify, collect, and preserve digital evidence from diverse IoT devices, including vehicles, wearables, and medical systems. Recent implementations, such as advanced IoT forensic analytics systems, demonstrate practical capabilities in extracting and analyzing data while maintaining legal admissibility. Overall, these efforts aim to provide scalable, efficient, and legally sound approaches for evidence identification and acquisition in complex, heterogeneous IoT environments.