Forensic science has historically relied heavily on tool mark analysis, which links tools to trace evidence by examining impression imprints. However, traditional methods continue to rely on examiner-driven visual comparison, which raises ongoing questions about reproducibility, transparency, and the validity of evidence in court. New advances in computer vision and deep learning are changing this field by providing unbiased, data-driven methods for source identification and tool mark categorization. With a focus on convolutional and contrastive architectures, this study explores the potential of advanced neural network models for automated similarity evaluation, classification, and likelihood ratio estimation in forensic tool mark evidence. The investigation demonstrates that multivariate neural networks routinely outperform conventional correlation-based and statistical approaches, producing higher accuracy and lower error rates, using carefully selected datasets of consecutively made tools and fired cartridge cases. Extensive data augmentation and interpretability frameworks minimize key technical issues, such as feature extraction under varying angles and substrates and dataset restrictions. According to benchmarking data, deep learning significantly improves the ability to distinguish between impressions from the same source and those from distinct sources, achieving over 95% sensitivity and specificity. Beyond performance, the study outlines paths for digital traceability and explainable AI integration, emphasizing the significance of algorithmic transparency, validation standards, and trial admissibility. The results provide useful suggestions for forensic professionals, legislators, and developers, advancing the transition from subjective examiner assessments to repeatable, algorithm-driven attribution. In the end, this work establishes the foundation for tool mark analysis in contemporary forensic practice that is more dependable, systematic, and legally defendable.