Fake currency detection involves determining whether a given currency sample is genuine or counterfeit. Counterfeiting poses a serious threat to financial systems worldwide, prompting the need for advanced detection methods. This study introduces a novel approach to identifying counterfeit currency through the application of Convolutional Neural Networks (CNNs). CNNs are specialized computer programs that excel at learning from images, making them ideal for distinguishing between genuine and fake banknotes. By training the CNN model with a diverse dataset containing images of both authentic and counterfeit currency, we can equip it with the ability to accurately classify banknotes based on subtle visual cues. The research showcases the effectiveness of CNN deep learning in detecting fake currency, highlighting its potential to enhance security measures within financial institutions. By automating the process of counterfeit detection, this technology can significantly reduce the risk of fraudulent activities and bolster trust in monetary transactions. The results of this study demonstrate the promising role of CNNs in safeguarding economies against counterfeit threats, underscoring the importance of leveraging advanced technologies for ensuring the integrity of financial systems