Machine learning techniques play a transformative role in the examination and analysis phases of mobile cloud forensics by enabling intelligent, automated, and scalable evidence processing. Research in this area focuses on developing ML-based frameworks for identifying, classifying, and correlating digital artifacts across mobile devices and cloud environments. Key topics include anomaly detection in cloud-stored mobile data, user behavior profiling, malware and app tampering detection, and data recovery through predictive modeling. Other important research directions involve deep learning for multimedia evidence analysis, federated learning for privacy-preserving forensic collaboration, and NLP-based techniques for analyzing communication logs and text evidence. Additionally, integrating explainable AI (XAI) for transparent forensic decision-making, employing graph neural networks for relationship mapping among digital entities, and developing lightweight ML models for resource-constrained mobile environments are emerging as critical challenges and opportunities in modern mobile cloud forensics research.