In the last few years, the increasing advancement of technologies and Computer Science has permitted the adoption of Machine Learning (ML) techniques in various fields. This work proposes two applications of ML algorithms in civil engineering to forecast features of a post-disaster scenario of a district or a city. Python libraries have been used in both applications and performed on an Intel Xeon @ 2x2.2 CPU and a NVidia Tesla T4 GPU. The former application aims to forecast the extent of debris (EOD) produced by a structure after an earthquake. Eight different ML algorithms have been applied on an input dataset of 300 samples identified by 7 features: (i) structure material, (ii) number of stories, (iii) height of the building, (iv) year of construction, (v) earthquake magnitude, (vi) epicenter distance, and (vii) evaluation direction of debris extent.