An approach to detecting suicide risk in social media posts is presented, addressing the challenges of limited and imbalanced datasets. The proposed workflow combines large language models (LLMs), few-shot learning, and expert-supervised prompt optimization. To address class imbalance, a novel Iterative Dual-LLM Few-Shot Learning with Adaptive Prompt Refinement (IDFL-APR) method for dataset expansion is introduced. This method employs two LLMs: one for classifying posts using few-shot learning, and another for dynamically optimizing prompts, with expert oversight to prevent overfitting. The optimized LLM then identifies high-confidence samples from unclassified data, focusing on underrepresented categories. Back-translation techniques are further applied to enhance textual diversity and achieve dataset balance across all categories. Subsequently, the bloomz-3b model is fine-tuned using optimized hyperparameters, implementing an active learning strategy to iteratively augment the training set. The proposed approach significantly enhances suicide risk detection accuracy. The best-performing model achieved a weighted F1-score of 0.7154 on the test set provided by the Suicide Ideation Detection on Social Media Challenge, a track of the IEEE Big Data 2024 BigData Cup. These results demonstrate a robust solution to the inherent challenges in suicide detection tasks.