Efficient retrieval of wildfire-related imagery from large-scale remote sensing and aerial datasets is essential for early detection, monitoring, and disaster response. However, the limited availability of annotated data severely restricts the effectiveness of conventional supervised approaches. To address this challenge, we propose a parameter-free self-distillation framework that leverages language models and unlabeled imagery to enhance wildfire image retrieval. Our method first employs large language models (LLMs) to generate domain-specific textual descriptions of wildfire phenomena, covering diverse visual cues such as fire spread, smoke plumes, vegetation conditions, and multi-perspective observations. These descriptions are used to construct a text-based classifier that transfers semantic knowledge into the visual domain. Through a self-distillation process, the classifier produces pseudo-labels for unlabeled wildfire imagery, which are then used to iteratively refine the vision encoder in a parameter-efficient manner. Experiments on both satellite remote sensing and aerial datasets demonstrate substantial improvements over zero-shot baselines, yielding more accurate and robust retrieval performance under class imbalance, heterogeneous imaging conditions, and cross-dataset generalization. This framework highlights the potential of combining LLM-driven semantic enrichment with self-distillation for scalable, annotation-free wildfire monitoring and supports broader applications in label-scarce environmental image analysis.