This research, with a descriptive-analytical approach and in a pragmatic way, examines the evolution of housing architecture after COVID-19. Thirty experts in architecture and urban planning made up its statistical population. A combination of artificial intelligence algorithms, tree data mining techniques, and the FTOPSIS multi-criteria decision analysis method have been applied to the information analysis process. A panel of experts was formed to gather information, and the criteria of housing design that had evolved in the post-corona period were identified by designing a questionnaire on the Likert scale (i.e., a psychometric scale named after its inventor)—the primary influential factors in the pattern of housing architecture after the Corona period were determined. The findings showed a significant correlation between housing architecture in the post-corona period and people's health patterns. Results showed that the factors that have the most significant impact on the evolution of housing in the region under investigation include the new function of housing (0.152), changing behavioral interactions (0.152), design criteria (0.145) and the passive ventilation factor with a score of 0.113. Other results showed that the physical distance as well as quarantine and isolation components obtained a score of 0.359 and 0.328, respectively, and were identified as the most critical developments in housing architecture. Finally, data mining findings showed that the spatial quality variable in the first phase had the most significant influence on the growth and fortification of the evolution of housing components at the study area level and the promotion of the health of people in the community in the post-pandemic period.