In recent years, natural disasters such as earth-quakes and hurricanes have caused significant damage to buildings and infrastructure worldwide. As a result, there has been an increasing demand for efficient and accurate methods of assessing the extent of building damage to facilitate effective recovery efforts. One emerging technology that shows great promise in this area is Light Detection and Ranging (Li-DAR). Therefore, this paper proposes a novel detection framework utilizing textural feature extraction strategies for Li-DAR-based building damage detection. Li-DAR, a remote sensing technology, has ability to create detailed maps of buildings and other infrastructure, allowing for precise identification and measurement of damage caused by natural disasters. Integration of the popular paradigm Edge cloud continuum extends cloud’s capabilities to the edge of the network, enabling more effective post-disaster recovery efforts. Smart Li-DAR sensors pre-process the captured data and send it to the nearest edge device for further processing.. Inclusion of machine learning algorithms like K-means clustering algorithm here is used to classify the buildings into damaged and undamaged classes by analyzing the extracted textural features. The scheme can detect various types of building damage. The cloud server is utilized to store the processed maps. The integration of the Edge-Cloud Continuum (ECC) has added more value by reducing the network usage, and latency of the Li-DAR-based building damage detection system. ECC enables processing and analysis of data at the point of origin as well as large-scale data processing and storage in cloud-based systems. This proposed framework has shown promising results in preliminary experiments and has the potential to revolutionize post-disaster recovery efforts by providing efficient building damage maps.