In this article, we address the challenge of optimizing a VGG-16 Convolutional Neural Network (CNN) for efficient weapon detection on computationally and memory-constrained devices. We utilize a strategic blend of techniques, including transfer learning, pruning, and quantization. The VGG-16 architecture was chosen due to its relevance for rapid and accurate weapon detection in crowded settings. Real-life datasets are employed for training to ensure practical applicability. After splitting the dataset, we explore various pruning levels and focus on a 60% pruning rate, accompanied by quantization for enhanced efficiency. Notably, the quantized model excels in edge computing, showcasing a significant speedup of 8.23x on a Vim3 platform and 2.51x on a Raspberry Pi 4, highlighting the effectiveness of our strategies compared to their implementation on a GPU-based platform. We take advantage of Open Neural Network Exchange (ONNX) quantization tools to perform accurate numerical conversions, resulting in faster inference speeds and more efficient resource utilization. In addition, analysis of memory consumption reveals substantial reductions, making it especially advantageous for edge computing