Provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. There is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (PPC) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (CD) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (ACI-CD) sensitivity prediction value of the plurality of patterns by inferring an ACI-CD prediction value of the plurality of patterns; and determining a correction rate of the layout CD of the plurality of patterns using the estimated sensitivity prediction value.