The machine learning technique used to find patterns in data without predefined labels is known as unsupervised learning. This approach allows algorithms to identify clusters, associations, or underlying structures in datasets without prior examples of correct outputs. Common applications of unsupervised learning include customer segmentation, anomaly detection, and topic modeling. By analyzing large datasets, unsupervised learning helps derive valuable insights and identify trends that may not be immediately apparent.