A decision tree problem involves making a series of decisions based on a set of conditions, represented in a tree-like model. Each internal node of the tree represents a decision based on a feature, branches represent possible outcomes, and leaf nodes indicate the final decision or classification. Decision tree problems are common in machine learning, where they are used for classification and regression tasks by recursively splitting data based on feature values. They help in decision-making processes, such as diagnosing diseases, credit risk assessment, and customer segmentation, by providing a clear and interpretable decision structure.