Large language models (LLMs) offer new opportunities in diabetes research by integrating contextual behavioral data with physiological biosignals, supporting more personalized care. This review surveys recent studies on the application of LLMs to interpret interactions among stress, physical activity, and glucose dynamics across type 1, type 2, gestational, and monogenic diabetes (MODY). We examine methods for capturing contextual data beyond traditional patient diaries, including wearable sensors, lifestyle logs, and digital health tools, and discuss how these data are combined with continuous glucose monitoring. After retrieving 39 relevant studies and finally retaining 25 after screening, we summarize the current capabilities, limitations, and clinical implications of LLM-assisted multimodal approaches in diabetes management. The findings highlight both the promise and challenges of applying LLMs to synthesize heterogeneous data, providing insights for future research on enhancing individualized and evidence-based diabetes care.