<p>Determining whether a quantum state is separable or entangled—the separability problem—is a fundamental challenge in quantum information science. Despite decades of research yielding numerous separability criteria, the problem is known to be NP-hard in the general case, lacking an efficient, scalable solution for high-dimensional systems. In recent years, machine learning (ML) has emerged as a powerful, data-driven paradigm to tackle this challenge. By leveraging its capacity to identify subtle patterns and approximate complex functions from data, ML offers a promising computational alternative to purely analytical methods. In this work, we synthesize the diverse applications of machine learning to the entanglement problem. We systematically review the use of key ML models—such as Neural Networks, Support Vector Machines, and K-Nearest Neighbors— for two primary tasks: the binary classification of states (the separability problem) and the quantitative estimation of entanglement measures like concurrence and negativity.</p>