<p>Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quantum science owing to the exponential scaling of the Hilbert space with respect to system size. Recent advances in artificial intelligence (AI), with its aptitude for high-dimensional pattern recognition and function approximation, have emerged as a powerful tool to address this challenge. A growing body of research has leveraged AI to represent and characterize scalable quantum systems, spanning from theoretical foundations to experimental realizations. Depending on how previous knowledge and learning architectures are incorporated, the integration of AI into quantum system characterization can be categorized into three synergistic paradigms: machine learning, deep learning and language models. This Technical Review discusses how each of these AI paradigms contributes to two core tasks in representing and characterizing quantum systems: quantum property prediction and quantum system reconstruction. These tasks underlie a range of applications, from quantum certification and benchmarking to enhancing quantum algorithms and identifying critical quantum phenomena. We also discuss key challenges and open questions, together with future prospects at the interface of AI and quantum science.</p>