Analyzing and classifying atmospheric circulation patterns (CPs) is useful for studying climate variability. These classifications can effectively identify the links between large-scale and regional-local scale processes. This work uses the historical (1975–2014) and projected (2015–2054) simulations of the MPI-ESM1-2-HR model to reproduce the CPs over the Middle East and Iran. Eighteen CPs were identified based on the geopotential height (GPH) of 500 hPa data from Coupled Model Intercomparison Project Phase 6 (CMIP6) in SSP1-2.6, SSP3-7.0, and SSP5-8.5. The method of principal component analysis (PCA) and k-means clustering was used. Then, the possible variability of each pattern in the surface and mid-level of the atmosphere and their expected changes in the frequency of CPs in global warming scenarios were investigated. This research showed that CPs 3, 6, and 11 happen during warm months of the year. The surface thermal low pressure is associated with the subtropical high in the atmosphere mid-level. According to the intensity of the low and the northward development, or the orbital expansion of the subtropical high, this pattern has an increasing (CPs 3 and 6) or decreasing (CP11) trend in the future period. CPs 1 and 12 occur during cold months. In CP1, dynamic high pressure prevails over Iran. However, in CP12, Iran is affected by high pressure from southeastern Europe. They will decrease in future projections. CPs 7 and 16, which often occur in the transition season (spring), show an increase in the projected patterns. CP 18 occurs throughout the year, but its highest frequency is in autumn, and the frequency of occurrence decreases. An increase in 500 hPa geopotential height over the Arabian Sea in all 18 classes and all three SSPs is predicted for future periods. Analysis of the obtained weather types indicates the identification of all effective atmospheric circulation patterns in the study area so that the behavior and frequency of each pattern explain the prevailing atmospheric phenomena in this region.