Latest research in Digital Forensic Identification and Collection focuses on developing advanced methodologies and tools to efficiently detect, acquire, and preserve digital evidence across diverse platforms, including computers, mobile devices, cloud services, and IoT networks. Studies emphasize the integration of artificial intelligence and machine learning to automate evidence identification, prioritize critical data, and streamline collection processes while ensuring legal admissibility and maintaining evidence integrity. Research highlights include comparative evaluations of open-source and commercial forensic tools, surveys of practitioner practices, and initiatives to create indigenous forensic toolkits and image repositories. Additionally, emerging frameworks address challenges posed by encrypted, damaged, or counterfeit storage devices, while case studies demonstrate the practical effectiveness of these approaches in enhancing investigative efficiency, accuracy, and reliability in modern digital environments.