Forensic science is changing more and more due to machine learning (ML), especially in ballistics analysis and firearm identification. Conventional techniques are labor-intensive and prone to human error since they frequently involve the manual examination of bullet striations, cartridge case marks, and other physical characteristics. Promising approaches to automate and improve the accuracy of these procedures are provided by recent developments in machine learning. ML algorithms can learn to identify complex patterns and features that can be too delicate for human recognition by utilizing vast datasets of ballistic images and attributes. By classifying and matching discharged bullets and cartridge cases to particular firearms, machine learning models can reduce the need for subjective expertise in firearm identification and produce quicker, more accurate answers. Convolutional neural networks (CNNs), for instance, have shown remarkable performance in recognizing distinctive firearm marks. Similar to this, deep learning methods can increase the accuracy of ballistic evidence comparisons, making it easier to find connections between suspects, crime scenes, and firearms. Additionally, ML models can aid with database management by more effectively organizing and analyzing large amounts of evidence connected to firearms, which enables forensic specialists to find connections across enormous networks of occurrences and firearms. Even if there are still obstacles to overcome, like the requirement for uniform data sets and the inconsistency of firearm markings, the incorporation of machine learning into ballistics analysis marks a substantial advancement in forensic inquiry. Machine learning is revolutionizing forensic ballistics by improving efficiency, accuracy, and objectivity. This has the potential to greatly enhance criminal investigations and legal outcomes.