Latest research in Machine Learning-assisted Evidence Identification in Mobile Cloud Forensics focuses on leveraging advanced ML algorithms to enhance the detection, classification, and analysis of digital evidence across mobile devices and cloud platforms. Studies explore techniques such as anomaly detection, pattern recognition, and natural language processing to address challenges like large-scale data volumes, encryption, and multi-tenant environments. Research highlights systematic frameworks that integrate ML models with mobile-cloud forensic workflows, enabling automated evidence identification, prioritization, and correlation. These approaches improve the accuracy, efficiency, and reliability of investigations, helping forensic professionals reconstruct events, detect suspicious activities, and maintain the integrity and admissibility of digital evidence in complex mobile-cloud ecosystems.