Extracting chemotherapy timelines from clinical narratives is a challenging task, but critical for cancer research and practice. In this paper, we present our approach and the research investigation we conducted to participate in Subtask 1 of the ChemoTimelines 2025 shared task on predicting temporal relations between pre-identified events and time expressions. We evaluated multiple fine-tuned large language models for the task. We used supervised fine-tuning strategies for Llama3-8B model to classify temporal relations. Further, we set up zero-shot prompting for Qwen3-14B to normalize time expressions. We also pre-trained and fine-tuned a Llama3-3B model using unlabeled notes and achieved results comparable with the fine-tuned Llama3-8B model. Our results demonstrate the effectiveness of fine-tuning and continual pre-training strategies in adapting large language models to domain-specific tasks.