The novel class of composite materials known as polyethylene-carbon nanotube composites (PECNTs) has attracted significant interest from scientists. In this study, authors investigated how artificial intelligence (AI) is employed to calculate the elastic modulus of PECNTs. For the first time, an AI-based modeling methodology replaces nanoindentation techniques like depth sensing indentation (DSI). This study highlights the complexities inherent in traditional methods, where the proposed methodology utilizes a gene expression programming (GEP) model, addressing challenges associated with accuracy in PECNT simulation. The proposed AI model test uses 135 input/output data pairs taken from the literature and randomly split into 82 training and 53 testing sets. The elastic modulus (EM) whichever dynamic E′ or quasi-static E) employs as an output factor in the models created, with the method of analysis, matrix type, processing technique, nanofiller type, and its content serving as inputs. Though the modeling progression is complete with results from the training and testing sets, the nanometer sensitivity of the prominent designs of the AI model displayed significant promise for the effective application of artificial intelligence methods in measuring the elastic modulus of PECNTs through non-destructive testing.