Artificial intelligence (AI) is transforming the landscape of legal forensics by enhancing the precision, speed, and scalability of evidence analysis and investigative procedures. Grounded in core principles such as pattern recognition, natural language processing (NLP), and machine learning (ML), AI-driven forensic tools enable the automation of document review, audio/video authentication, predictive modelling, and data extraction from unstructured sources. These technologies are increasingly deployed in tasks such as digital evidence triage, case law analysis, and fraud detection, offering measurable benefits in both criminal and civil litigation. Emerging advancements in AI—such as explainable AI (XAI), deep fake detection, and quantum-enhanced algorithms—are further expanding forensic capabilities, while addressing concerns over transparency, ethics, and admissibility of AI-generated evidence. Despite these innovations, legal frameworks remain challenged by questions of accountability, bias mitigation, and standards for algorithmic reliability. This evolving interplay between AI technology and legal standards necessitates a multidisciplinary approach to ensure AI’s responsible integration into forensic practice. This abstract provides an overview of the foundational principles, current applications, and future trajectories of AI in legal forensics, highlighting both its transformative potential and the critical considerations that must guide its adoption.