Vision-Language Models (VLMs) are multimodal AI systems that seamlessly integrate visual perception and natural language understanding, enabling machines to reason about images, videos, and text simultaneously. These models power applications from visual question answering to zero-shot image classification, fundamentally changing how AI interprets the visual world. At their core, VLMs learn a shared embedding space where semantically similar images and text descriptions cluster together—a capability that emerged from contrastive learning techniques pioneered by CLIP and refined by subsequent architectures. A critical insight: the quality of vision-language alignment depends not just on model architecture, but on how effectively the model bridges the modality gap between continuous visual features and discrete linguistic tokens through specialized fusion mechanisms.