The growing adoption of Unmanned Aerial Vehicles (UAVs) across civilian, commercial, and security sectors has introduced new challenges for digital forensic investigations, particularly in incidents involving unauthorized surveillance, privacy violations, and cyberattacks. The recovery and analysis of UAV-generated evidence remain challenging due to heterogeneous platform architectures, proprietary and encrypted data formats, and the absence of standardized forensic procedures. This paper presents a PRISMA-based systematic review of more than 70 peer-reviewed studies, organized into six research themes: forensic frameworks and standardization, evidence acquisition and analysis, forensic tool evaluation, cybersecurity and anti-forensics, AI-assisted investigations, and application-oriented studies. Unlike previous surveys that primarily examine individual aspects of drone forensics, this review brings together technical, operational, and legal perspectives to identify common challenges and their implications for practical investigations. The findings reveal persistent gaps in tool interoperability, forensic readiness, evidence preservation, real-world validation, and legal admissibility. Emerging technologies, including machine learning, digital twins, cryptanalysis, and Large Language Models (LLMs), offer opportunities to improve evidence correlation, incident reconstruction, anomaly detection, and forensic reporting. However, their practical adoption requires careful consideration of explainability, reproducibility, data privacy, and the reliability of AI-generated findings. Based on the identified gaps, the review proposes future research directions emphasizing standardized evidence acquisition, forensic-ready UAV architectures, interoperable tools, and verifiable AI-assisted analysis. The study provides practical insights for forensic investigators, policymakers, and tool developers toward establishing more reliable, scalable, and legally defensible UAV forensic practices.
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Wadhwa et al. (2026) studied this question.
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