Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across variables. Existing ISTS forecasting methods often solely utilize historical observations to predict future ones while falling short in learning contextual semantics and fine-grained temporal patterns. To address these problems, we achieve MM-ISTS, a multimodal framework augmented by vision-text large language models, that bridges temporal, visual, and textual modalities, facilitating ISTS forecasting. MM-ISTS encompasses a novel two-stage encoding mechanism. In particular, a cross-modal vision-text encoding module is proposed to automatically generate informative visual images and textual data, enabling the capture of intricate temporal patterns and comprehensive contextual understanding, in collaboration with multimodal LLMs (MLLMs). In parallel, ISTS encoding extracts complementary yet enriched temporal features from historical ISTS observations, including multi-view embedding fusion and a temporal-variable encoder. Further, we propose an adaptive query-based feature extractor to compress the learned tokens of MLLMs, filtering out small-scale useful knowledge, which in turn reduces computational costs. In addition, a multimodal alignment module with modality-aware gating is designed to alleviate the modality gap across ISTS, images, and text. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions.