Common pitfalls in model evaluation include not setting inference mode, mismatched batch sizes, inconsistent preprocessing, incorrect dataset shuffling, and misaligned labels. Proper handling of sample weights, metric thresholds, and custom metric resets is essential for accurate TensorFlow model evaluation results.