Precise identification of multinational currency denominations is crucial for developing assistive technology that enhances the lives of visually impaired individuals. This study estimates how well deep learning models including CNN, ResNet50, VGG16, and MobileNet19 recognize different currency denominations. A multiple datasets of currency images comprising CAD, Euro, USD, Pounds, and INR was curated and annotated, that represent various denominations for each currency, and real-world imaging challenges, such as variations in lighting and angles, were employed for testing. Four models were executed and evaluated for performance; that includes traditional Convolutional Neural Networks (CNNs) and some pre-trained models such as ResNet50, VGG16, and MobileNet19. The performance of these models were measured on metrics such as accuracy, precision, recall, and F1-score. The test images of currency notes were considered across different lighting conditions and image qualities, which were used to standardize the models. A comparative assessment of these models discloses that the pre-trained model ResNet50 offers better performance in the evaluation. These observations provide insights to enhance the reliability and efficiency of currency recognition systems, driving the way for innovative solutions such as smart glasses and assistive technologies that empower individuals with visual impairments in their day-to-day.