Recent advances in artificial intelligence have enabled the effective utilization of Vision-Language Models (VLMs), which integrate textual and visual data into a shared representation space, across various disciplines. Transcending traditional single-modal systems, these innovative models offer holistic approaches that support contextual interpretation, multimodal reasoning, and task-oriented generation processes. While this study thoroughly examines the fundamental architectural components of Large Language Models (LLMs) and VLM-based approaches, its application dimension aims to focus predominantly on the sectoral integration of vision-language models. First, the study technically analyzes the Transformer architecture—the foundation of both model families—through mechanisms like self-attention, tokenization, and positional encoding to elucidate the operating principles of LLMs. Subsequently, the focus shifts entirely to multimodal frameworks, detailing the specific application areas of VLMs and their action-augmented Vision-Language-Action (VLA) counterparts. In this context, current approaches are evaluated in domains including end-to-end planning in autonomous driving, spatial grounding in robotics, structured information extraction in healthcare, remote sensing and domain-specific diagnostics in agriculture, and human-centric visual analysis tasks. The findings indicate that VLM-based systems are evolving from passive information processors into decision-support components providing high-level semantic guidance. However, bottlenecks encountered during real-world deployment, such as computational costs, real-time constraints, and hallucination risks, are discussed, concluding that future trends will pivot towards parameter-efficient and domain-specific hybrid architectures.