Traditional language model compression techniques, like knowledge distillation, require a fixed architecture, limiting flexibility, while structured pruning methods often fail to preserve performance. This paper introduces Iterative Structured Knowledge Distillation (ISKD), which integrates knowledge distillation and structured pruning by progressively replacing transformer blocks with smaller, efficient versions during training. This study validates ISKD on two transformer-based language models: GPT-2 and Phi-1. ISKD outperforms L1 pruning and achieves similar performance to knowledge distillation while offering greater flexibility. ISKD reduces model parameters - 30.68% for GPT-2 and 30.16% for Phi-1 - while maintaining at least four-fifths of performance on both language modeling and commonsense reasoning tasks. These findings suggest that this method offers a promising balance between model efficiency and accuracy.