The field of forensics relies heavily on the investigation and categorization of bite marks. Bite mark identification is a kind of forensic science that uses the unique characteristics of tooth marks left on skin or other soft tissues to positively identify a person. When compared to DNA analysis, bite mark analysis is devoid of scientific proof and sometimes only catches a small part of the tooth, which greatly complicates identification. After extracting bite marks using a Gabor filter, the suggested technique employs a convolutional neural network (CNN) based on inception and an auxiliary layer. Human forensic odontologists may introduce bias into bite mark analysis. The suggested model, which can provide more objective identification, has been trained on massive datasets. In addition, we suggest using a model to extract fine information from bite mark pictures that a human eye may overlook, which could lead to more precise identifications. According to the Interpol Disaster Victim Identification (DVI) Guide, teeth are one of the three main identifiers with DNA and fingerprints, making the work of dentists crucial in completing criminal cases. Using their own dataset, they ran a series of simulations to prove how much superior the offered models were than the competition. The results proved that the suggested model was the best option.