Evidence triaging in mobile forensics focuses on efficiently prioritizing and categorizing digital evidence from mobile devices to accelerate investigations and optimize resource utilization. Research in this area emphasizes developing intelligent and automated triaging frameworks capable of identifying high-value evidence while reducing manual effort. Key topics include machine learning–assisted prioritization of artifacts, real-time filtering of relevant data from apps, messages, and multimedia, and rapid assessment of volatile and non-volatile storage. Other significant areas involve correlation of mobile and cloud data for comprehensive triaging, anomaly detection for suspicious activities, and automated metadata analysis for quick evidence evaluation. Emerging directions explore AI-driven predictive triaging, privacy-preserving triage mechanisms, standardized triaging procedures for multi-platform mobile devices, and integration with forensic readiness models. Addressing challenges such as large data volumes, encrypted content, and heterogeneous mobile operating systems is essential for advancing research in evidence triaging for mobile forensics.