Artificial Intelligence (AI) has achieved a significant milestone by demonstrating the ability to perceive and even be fooled by optical illusions, mirroring human visual experiences. This development is not merely a technical feat but provides a powerful new lens through which to study the intricacies of the human brain. Studies dating back to 2018 have shown deep neural networks (DNNs), developed with reference to the brain's structure, are susceptible to illusions like the 'Rotating Snake Illusion,' suggesting shared processing mechanisms. More recent research, including studies published in 2024 and 2026, details how AI systems, sometimes enhanced with quantum mechanics principles, can interpret ambiguous figures such as the Necker Cube and Rubin's Vase, switching between interpretations much like humans. This capability is shedding light on 'predictive coding theory,' which posits that the brain anticipates visual information based on past experiences, sometimes leading to misperceptions. Experts note that when AI replicates these illusions, it offers valuable computational models that help cognitive scientists understand the underlying processes of human visual perception that cannot be directly measured in the brain. However, the research also highlights differences; while AI can be fooled, it may not perceive illusions in precisely the same way as humans, often lacking mechanisms like selective attention or exhibiting a 'visual bias' compared to human semantic understanding. Despite these divergences, comparing how humans and AI respond to illusions helps identify common perceptual biases and unique vulnerabilities in each system, ultimately contributing to more robust AI and a deeper understanding of human cognition. This ongoing research at the intersection of AI and cognitive science, involving institutions globally, underscores AI's role not just as a tool but as a partner in unraveling the mysteries of the mind, with implications for fields from neuroscience to computer vision.