  The Bin Packing Problem (BPP) is a well-established combinatorial
optimization (CO) problem. Since it has many applications in our daily life,
e.g. logistics and resource allocation, people are seeking efficient bin
packing algorithms. On the other hand, researchers have been making constant
advances in machine learning (ML), which is famous for its efficiency. In this
article, we first formulate BPP, introducing its variants and practical
constraints. Then, a comprehensive survey on ML for multi-dimensional BPP is
provided. We further collect some public benchmarks of 3D BPP, and evaluate
some online methods on the Cutting Stock Dataset. Finally, we share our
perspective on challenges and future directions in BPP. To the best of our
knowledge, this is the first systematic review of ML-related methods for BPP.
