Deep learning has found its way into modern criminal identification and investigation systems. In this paper, we illustrate the automation of facial recognition system using artificial intelligence techniques, through the development of “Criminal Face Identification” GUI using MATLAB. The application allows users to upload images, which are subsequently pre-processed, analysed with HOG feature extraction applied, and classified by a pre-trained SVM model. The system predicts the identities using trained criminal datasets and provides instant feedback. Recent studies highlight how the utility of CNNs, RNNs, and hybrid deep learning models are increasingly relevant to surveillance, behavioural analysis and classification, review of legal documents, and spatiotemporal crime forecasting. Deep learning is indispensable in today’s crime analytics ecosystem, as the reviewed literature demonstrates its ability to address complex, high-dimensional, and unstructured data. Further supporting the broader evidence that AI-driven applications are crucial for the development of accurate, efficient, and scalable tools to aid in both real-time criminal detections, as well as the augmentation of public safety measures.