<p>A significant portion of India's tourism sector is dependent on the Himalayan states due to their majestic beauty. The remote communities residing in these scenic Himalayan landscapes are also economically dependent on the tourism activities. However, these states are affected by numerous natural disasters, which hinder the tourism activities and make the indigenous communities vulnerable as well. The connectivity of these remote locations is mainly dependent on the road network, which is one of the most affected infrastructures during these disasters. The nexus of the development of tourism and the Socio-economic upliftment of remote communities is highly dependent on the accessibility of these road networks. Hence, the identification of the risk exposure and vulnerability of the network is essential to build disaster-resilient infrastructure in these areas. Different spatial analyses are utilised to determine the risk exposure of the networks, while network analysis is employed to understand the relative importance of individual stretches. This approach is used for the ultimate classification of the road network using machine learning algorithms for ‘Response Prioritisation’ and to obtain a comprehensive understanding. The study is helpful in designing an effective and efficient framework for identifying and prioritising the road network in emergency cases, as well as for improving operation and maintenance.</p>