The safety, efficiency, and longevity of global railway networks are directly linked to the rigorous inspection and management of their vast inventory of physical assets. Over the past decade, the field has progressed from manual surveys to automated systems leveraging imagery from track-based or aerial platforms. These systems predominantly built on traditional Computer Vision (CV) models have proven effective at detecting a pre-defined set of common assets. However, this progress has exposed a fundamental architectural and operational ceiling: the closed-world assumption. Current models are constrained to a fixed catalogue of classes defined during their training. It makes the model incapable of identifying novel objects or adapting to environmental changes without costly and continuous cycles of data re-annotation, retraining, and redeployment. This review paper argues that Vision-Language Models (VLMs), a paradigm whose rapid maturation is evidenced by recent comprehensive surveys offer a transformative solution. We provide a focused overview of the limitations of current CV systems and map the mechanics of a VLM-powered approach specifically Open-Vocabulary Detection and Reasoning Segmentation directly to the outstanding challenges in rail asset management. Ultimately, the literature suggests that the adoption of VLMs could catalyze a fundamental shift in railway infrastructure management that serves as a key enabler for next-generation Predictive Maintenance and autonomous Digital Twins.