The scale, heterogeneity, and velocity of modern digital evidence have outpaced traditional forensic workflows, while existing AI-based solutions often lack explainability and legal defensibility. This chapter proposes an AI-driven digital forensic framework integrating automated evidence acquisition, multimodal correlation, explainable inference, and human-in-the-loop validation. The methodology emphasizes behavior-centric reconstruction, confidence quantification, and adversarial robustness to ensure forensic soundness. Experimental evaluation on heterogeneous forensic datasets shows that the proposed framework achieves 91.6% evidence detection accuracy, reducing the false positive rate to 7.3%, compared to 82–87% accuracy and 10–13% false positives reported in recent AI-based forensic approaches. Investigator trust scores improved to 4.6/5, outperforming post-hoc explainability baselines (3.4–4.2/5), while maintaining near-linear scalability under increasing evidence volume.