Intelligent technologies reshape district heating systems towards greater efficiency, cost-effectiveness, and sustainability. Addressing the challenges of their full implementation requires a clear understanding of the current research landscape. This paper presents a scoping review of post-2000 literature from the Web of Science, focusing on the current state, emerging trends, and limitations of intelligent technologies for district heating systems. The review reveals a significant interest in machine learning for heat load prediction, operational efficiency, and fault detection, with an emerging focus on deep learning and hybrid models. However, research gaps remain, particularly in unsupervised learning, real-world case studies, and the integration of IoT and cloud computing, underscoring the need for further research. The result also shows the need for innovative solutions to enhance the resilience and adaptability of district heating systems within the rapidly evolving energy landscape.