Association rule learning is a machine learning method used to discover interesting relations between variables in large databases, often applied in market basket analysis to identify sets of items that frequently co-occur in transactions. It involves generating rules that highlight associations, with a focus on metrics like support, confidence, and lift to evaluate the strength and relevance of these rules.