Multiple Instance Learning (MIL) is a specialized area of machine learning that addresses problems where training data is organized into labeled bags of instances rather than individually labeled samples, making it suitable for scenarios with weak or ambiguous supervision. Foundational research introduced key MIL assumptions, such as a bag being labeled positive if at least one instance is positive, and negative otherwise, leading to algorithms based on instance-level inference, bag-level kernels, and embedding approaches. Recent studies leverage deep learning to develop attention-based MIL, convolutional MIL for image and histopathology analysis, graph-based MIL for relational data, and pooling mechanisms that aggregate instance features effectively. Applications span medical imaging, drug activity prediction, object detection, text classification, and bioinformatics, demonstrating MIL’s capability to handle noisy labels, partial annotations, and complex hierarchical data structures. Current research also explores integrating MIL with transfer learning, self-supervised learning, and contrastive learning to improve generalization, interpretability, and robustness in real-world tasks.