In this paper, we present a forensic perspective on classifying gunshot wound patterns using Deep Learning (DL). Although DL has revolutionized various medical specialties, such as automating tasks like medical image classification, its applications in forensic contexts have been limited despite the inherently visual nature of the field. This study investigates the application of DL techniques (59 architectures) to classify gunshot wounds in a forensic context, focusing on distinguishing between entry and exit wounds and determining the Medical-Legal Shooting Distance (MLSD), which classifies wounds as contact, close range, or distant, based on digital images from real crime scene cases. A comprehensive database was constructed with 2,551 images, including 1,883 entries and 668 exit wounds. The ResNet152 architecture demonstrated superior performance in both entry and exit wound classification and MLSD categorization. For the first task, achieved accuracy of 86.90% and an AUC of 82.09%. For MLSD, the ResNet152 showed an accuracy of 92.48% and AUC up to 94.36%, though sample imbalance affected the metrics. Our findings underscore the challenges of standardizing wound images due to varying capture conditions but reflect the practical realities of forensic work. This research highlights the significant potential of DL in enhancing forensic pathology practices, advocating for Artificial Intelligence (AI) as a supportive tool to complement human expertise in forensic investigations.