Thermal to visual cross-domain face recognition is the process of recognising faces captured in the thermal domain and matching them with the visual domain images. Its significance lies in enabling accurate face recognition across different domains, allowing for enhanced security and surveillance capabilities in various environments, particularly during night-time or adverse weather conditions where thermal imaging excels. By bridging the gap between thermal and visual domains, this cross-domain face recognition improves the overall performance and applicability of face recognition systems in real-world scenarios. This survey explores the existing literature on thermal-to-visual cross-domain face recognition, categorising it into two distinct approaches: the first classification is based on the learning model, and the other is on generalisation approaches. This survey aims to provide a nuanced understanding of the current landscape, methodologies, and challenges in this evolving field by systematically reviewing and categorising the extensive body of research in thermal-to-visual cross-domain face recognition.