Pretrained model fine-tuning pitfalls include misaligned preprocessing, distribution shifts, overfitting, catastrophic forgetting, high computational costs, improper learning rate handling, batch norm issues, data leakage, and lack of checkpointing. Proper techniques ensure effective adaptation and model performance.