Model-Based Testing (MBT) is evolving rapidly as AI brings new levels of intelligence, speed, and adaptability to test design and execution. In 2025, AI-powered MBT enables teams to automatically generate, optimize, and maintain test models that reflect real user behavior and system complexity. This article explores how AI is transforming model-based testing by enhancing test coverage, reducing manual modeling effort, and enabling smarter exploration of application states. It also highlights the role of machine learning in identifying risk areas, prioritizing test paths, and adapting tests as systems change.