Fine-grained object detection in food computing is severely constrained by the vast diversity of food items and the high cost of data annotation. Existing Zero-Shot Food Detection (ZSFD) methods attempt to solve this by leveraging semantic information, but they suffer from two critical bottlenecks: (1) a "Semantic Dilemma" (SD) where textual descriptions are too ambiguous to distinguish visually similar food categories, and (2) an "Architectural Bottleneck" (AB) due to the granularity mismatch between high-level semantics and low-level visual features. In this article, we propose SyMFood (Synergistic Multi-modal Framework for Food Generalization), a novel ZSFD framework designed to systematically overcome these challenges. To resolve the SD, SyMFood employs a multi-modal prompt system, which combines rich descriptions from Large Language Models (LLMs) with unambiguous visual exemplars to provide precise semantic grounding. To break the AB, SyMFood introduces a “Refine-then-Fuse” architecture. This design first utilizes a Context-Aware Spatial-Channel Refinement (CaSC) block to enhance visual features independently. Subsequently, a Progressive Food Knowledge Fusion (ProFus) module performs bi-directional, iterative co-refinement between the enhanced visual features and multi-modal prompts across all scales. Extensive experiments across four challenging datasets, including food-specific (UEC FOOD 256, FOWA) and general-purpose (PASCAL VOC, MS COCO) benchmarks, validate our approach. The proposed method outperforms baselines, yielding a notable 8.5% improvement in Harmonic Mean on the FOWA dataset in the genearl ZSD (GZSD) setting. The source code will be available at https://github.com/Niko000202/SymFood0202.