Abstract: Multi-instance multi-label learning (MIML) is an important machine learning framework designed to address complex tasks where each object is represented as a bag of multiple instances and can be associated with multiple labels simultaneously. It has been widely applied in domains such as drug activity prediction and medical image analysis. Different from traditional multi-instance learning and multi-label learning, MIML simultaneously captures the diversity in both the input space (multiple instances) and the output space (multiple labels), enabling more comprehensive modeling and understanding of real-world complex objects. However, this expressiveness also brings significant challenges, including high computational complexity, difficulties in model optimization, and limited generalization capabilities. Therefore, MIML has attracted increasing attention from the research community. Despite this growing interest, a comprehensive survey of MIML is still lacking. This paper first introduces the formal problem definition and notations related to MIML. Then, from the perspective of data complexity, we categorize MIML problems into three main types: standard MIML, multi-source MIML, and MIML with imprecise labeling. These are further refined into nine subcategories based on solution strategies, instance sources, and label completeness, which are systematically discussed and analyzed. We also summarize commonly used datasets and benchmark comparisons for MIML methods. Finally, we present five representative application scenarios of MIML in real-world tasks, outline four promising future research directions, and conclude the paper.