The ability to instantly identify different currencies is crucial in the present day. As an integral part of today’s global automation infrastructure, the money identification system is a highly sophisticated and crucially important AI system. In this work, we offer a currency recognition system that works well with Pakistani cash. There are a variety of currency-detecting apps available nowadays. This work focuses on recognizing currency based on the coin’s physical features. Convolutional neural networks, a powerful type of DNN architecture, are considered to address the issue. The convolutional neural network has delivered the best performance on raw data and the ability to extract valuable features, whereas most other architectures fail to even get close. Pakistan uses seven different varieties of paper currency, all of which are affected by the proposed scheme. Initially, an image of currency is taken as input and subjected to several preprocessing phases, and a region of interest (ROI) is isolated from the surrounding scene. Finally, binary descriptors are also obtained during feature extraction. In Hamming Distance, matching is performed using these binary descriptors. The experimental findings confirmed that our suggested method might be used in practice to recognize unfamiliar money paper images with an accuracy of 95% or better.