Carbon/carbon-silicon carbide (C/C-SiC) composites are critical thermostructural materials for extreme aerospace environments, where flexural strength governs structural reliability. However, reliable prediction of flexural strength remains difficult because flexural strength is governed by complex nonlinear process–structure–property relationships, while experimental evaluation is time-consuming and costly. Traditional ML models often struggle to capture the complex structure-property relationships embedded in unstructured textual descriptions. Despite large language models (LLMs) excelling in natural language, their direct application to small-sample numerical regression remains challenging. This study introduces a semantics-guided multimodal machine learning (SGMML) framework using a LoRA-fine-tuned LLM text encoder with structured numerical features. The SGMML model based on a dataset of 142 cases achieved an MAE of 25.96 MPa and an R² of 0.81 outperforming traditional ML models by 20.9% and fine-tuned LLMs by 92.9% in terms of R². Experimental validation on two C/C-SiC composites manufactured via different processes yielded prediction residuals of 2.1 and 1.5 MPa. Furthermore, independent validation on external literature samples yielded an MAE of 18.08 MPa, demonstrating the framework's transferability beyond the training data. These results show that combining structured descriptors with domain-specific textual encoding enables accurate and scalable property prediction for advanced composites under data-limited conditions.