Machine learning-assisted evidence identification in mobile cloud environments focuses on automating the discovery, classification, and prioritization of potential digital evidence from distributed mobile and cloud data sources. Research in this area aims to develop intelligent frameworks capable of recognizing forensic artifacts across multiple platforms and services while maintaining data integrity and privacy. Key topics include supervised and unsupervised learning for evidence pattern recognition, anomaly detection in user activities, and feature selection for artifact relevance assessment. Emerging directions involve deep learning for multimedia evidence extraction, federated learning for decentralized forensic collaboration, and NLP-based techniques for analyzing textual and communication evidence. Furthermore, integrating explainable AI for interpretable decision-making, leveraging reinforcement learning for adaptive evidence discovery, and applying graph-based learning for relationship mapping among entities are gaining prominence. These research efforts collectively enhance the accuracy, efficiency, and reliability of digital investigations in mobile cloud forensics.