Abstract

The widespread adoption of unmanned aerial vehicles (UAVs) in critical operations has intensified the need for advanced forensic capabilities following drone-related incidents. As conventional digital forensics struggle with the scale and complexity of UAV data, artificial i ntelligence ( AI) h as emerged as a transformative tool for post-incident analysis. This paper presents a systematic literature review (SLR) of AI-assisted techniques in drone forensics, synthesizing recent contributions that apply machine learning and deep learning to flight log analysis, anomaly detection, multimedia evidence interpretation, and scene reconstruction. Unlike previous surveys, this work focuses specifically on the forensic dimension, offering a structured classification of methods, identifying key technical and legal challenges, and mapping the maturity of current approaches. Our findings reveal persistent gaps, including the absence of standardized datasets, limited real-world validation, and insufficient explainability of model outputs. To address these, we propose a research roadmap toward trustworthy, interoperable, and legally admissible forensic AI systems. By bridging disciplines and aligning with ethical and regulatory frameworks, this study contributes a forward-looking foundation for the development of robust forensic pipelines in UAV investigations.