Artificial Intelligence & Machine learning (AI/ML) have demonstrated unprecedented success in predicting complex quantum dynamics and accelerating scientific discovery. Yet, the principles underlying its success remain poorly understood. How do learning algorithms represent quantum systems? What makes their predictions reliable? Under what conditions do they produce unphysical or hallucinated predictions? Addressing these questions become essential for establishing the foundations of interpretable and physically consistent AI/ML for quantum science.​ Our research integrates AI/ML with the fundamental principles of quantum mechanics to develop physics-informed learning frameworks for understanding, predicting and controlling quantum dynamics. These insights further guide our efforts toward developing quantum-enhanced AI/ML algorithms for next-generation quantum technologies.