Association rule learning is a data mining technique focused on discovering interesting relationships, or association rules, among items within large datasets. It systematically identifies patterns of co-occurrence, typically expressed as 'if-then' statements, and quantifies their significance using metrics such as support and confidence to extract valuable insights from transactional or relational data.