Self-evolving AI systems represent a major shift in artificial intelligence design. Instead of relying solely on static training pipelines, these systems iteratively modify their own architectures, parameters, and learning strategies. They combine AutoML techniques, reinforcement learning, and evolutionary computation to continuously optimize performance across changing environments. This enables AI systems to adapt dynamically to complex domains such as financial markets, healthcare diagnostics, and autonomous decision-making systems. The primary advantage of this approach is the reduction of human dependency in model optimization. However, it introduces challenges related to stability, control, and alignment, requiring advanced governance mechanisms to ensure safe and predictable behavior.