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Abstract: The proliferation of counterfeit currency poses a significant threat to the global economy, necessitating the development of efficient and accurate detection methods. This paper presents a novel approach to fake currency detection using machine learning techniques. The proposed system leverages a combination of computer vision and machine learning algorithms to identify counterfeit banknotes with high accuracy. The system employs a convolutional neural network (CNN) to extract features from images of banknotes, which are then classified as genuine or fake using a support vector machine (SVM) classifier. The CNN is trained on a large dataset of images of genuine and fake banknotes, while the SVM is fine-tuned to optimize its performance. The Experimental results demonstrate that our proposed system achieves an accuracy of 98.5% in detecting fake currency, outperforming traditional methods based on manual inspection and rule-based systems. The system's performance has been evaluated using a comprehensive set of metrics, including precision, recall, F1-score, and receiver operating characteristic (ROC) curve analysis. The proposed system offers several advantages, including high accuracy, speed, and scalability, making it suitable for real-world applications in banking, finance, and law enforcement. Furthermore, the system's modular design enables easy integration with existing currency processing systems, facilitating seamless deployment. This research contributes to the development of intelligent systems for fake currency detection, providing a robust and reliable solution to combat counterfeiting.