Debugging TensorFlow data pipelines involves monitoring metrics like data loading times and dataset cardinality using tf.data tools. Visualization with TensorBoard and assertions via tf.debugging help identify anomalies. Custom callbacks enable performance logging to optimize pipeline efficiency and model training.