The field of vision-language modeling has progressed from handcrafted feature extraction to deep learning, and more recently, to few-shot and zero-shot learning techniques. These advanced approaches mitigate the dependency on large-scale labeled datasets by enabling models to generalize from minimal or no task-specific data. This survey traces the evolution of vision-language models, highlighting key developments from early feature-based methods to modern architectures that leverage contrastive learning, retrieval-augmented generation, and meta-learning. We examine state-of-the-art models such as CLIP, BLIP, and Flamingo, evaluating their strengths and limitations across tasks including visual question answering (VQA), image captioning generation, and cross-modal retrieval. Additionally, we analyze the challenges of domain generalization, computational efficiency, and interpretability in real-world applications. Finally, we explore future research directions to enhance adaptability, cross-modal integration, and practical deployment of vision-language models in few-shot learning scenarios.