The 2025 Grand Challenge on Multimedia Verification addresses the challenges of verifying the authenticity and context of online multimedia content. Participants analyzed real-world cases of images and videos, assessed their sources, detected any potential manipulations, and submitted detailed verification reports. The main competition was structured into three stages: Training, Validation, and Real-World Verification, which included live test cases and welcomed solutions ranging from manual methods and OSINT practices to automated tools and novel AI techniques. A total of 32 teams from 11 countries registered. The submitted solutions presented diverse pipelines that integrated forensic analysis, multimodal reasoning, and large language models (LLMs). The results demonstrate significant progress toward semi-automated verification workflows, while also exposing challenges in scalability, consistency, and reliability. In this paper, we present the challenge design, datasets, evaluation criteria, and participant outcomes, providing insights to guide future research and practice in multimedia verification.