As the world becomes more connected through digital platforms and smart devices, a flood of data is straining organizational systems' ability to comprehend and extract relevant information for sound decision-making. Dataset distillation is a technique that compresses the knowledge of large-scale datasets into smaller, synthetic datasets, allowing models to be trained with less data while achieving similar performance to models trained on full datasets. This approach was proposed by Wang et al. (2020), who successfully distilled the 60,000 training images in the MNIST dataset into a smaller set of synthetic images, achieving 94% accuracy on the LeNet architecture. Dataset distillation differs from core-set or instance selection, where a subset of data samples is chosen using heuristics or active learning, as it creates a smaller dataset that retains critical information, offering a more efficient and reliable approach for model training. The primary advantage of dataset distillation is its ability to encapsulate the knowledge and patterns of a large dataset into a smaller synthetic one, providing benefits such as efficient training, cost-effectiveness, better security and privacy, and faster experimentation. Various algorithms exist to generate synthetic examples from large datasets, including performance matching, parameter matching, distribution matching, and generative techniques. Dataset distillation has applications in continual learning, federated learning, neural architecture search, privacy and robustness, recommender systems, medicine, and fashion, where it helps reduce data size for optimal training and ensures data privacy while maintaining performance.