Recently, multi-modal learning is one of the important research fields of machine learning and data mining, and it has a wide range of practical applications, such as cross-media search, multi-language processing, auxiliary information click-through rate estimation, etc. Traditional multi-modal learning methods usually use the consistency or complementarity among modalities to design corresponding loss functions or regularization terms for joint training, thereby improving the single-modal and ensemble performance. However, in the open environment, affected by factors such as data missing and noise, multi-modal data is imbalanced, specifically manifested as insufficient or incomplete, resulting in “inconsistency modal feature representations” and “inconsistent modal alignment relationships”. Direct use of traditional multi-modal methods will even degrade single-modal and ensemble performance. To solve these problems, reliable multi-modal learning has been proposed and studied. This paper systematically summarizes and analyzes the progress made by domestic and international scholars on reliable multi-modal research, and the challenges that future research may face.