In contemporary society, public space security and safety are of utmost significance. The theft of wallets, a frequent type of street crime, puts people’s personal items at risk and may result in financial loss and psychological misery. By utilizing Edge Impulse technology to identify and expose wallet-snatching incidents in public areas, this article offers a fresh solution to the problem. To develop a reliable and effective wallet-snatching detection solution, the suggested system blends machine learning techniques with the strength of the Edge Impulse platform. This study used Spiking Neural Networks (SNNs) which are inspired by the biological neural networks found in the brain. Edge Impulse offers a thorough framework for gathering, preprocessing, and examining data, enabling the creation of extremely precise machine learning models. The system can accurately discriminate between legitimate interactions with wallets and suspicious snatching attempts by training these models on a dataset that includes both normal and snatching events. The efficiency of the suggested method is 95% demonstrated by experimental findings, which show high accuracy and low false positive rates in recognizing wallet snatching instances. Increasing public safety, giving people a sense of security in public places, and discouraging prospective wallet-snatching criminals are all goals of this research.