Counterfeit detection in banknotes remains a significant challenge, given the advanced techniques employed by counterfeits. Many existing solutions are either in accessible to the general public or lack the robustness required for reliable authentication. To overcome these limitations, this study proposes a web-based system for bank note verification, integrating machine learning and image processing. The system allows users to upload images of banknotes through a user-friendly interface designed with responsive web technologies, while backend operations are managed using Django. Image preprocessing methods, including Gaussian blurring, normalization, and Sobel edge detection, are applied to enhance visual quality and extract essential statistical features such as entropy, variance, skewness, and kurtosis. These features serve as inputs to a logistic regression model that classifies banknotes as authentic or counterfeit. Experimental results reveal that the proposed system achieves high accuracy on a balanced dataset. Additionally, comparative analysis with other machine learning classifiers shows that the system out performs existing state-of-the-art models, offering are liable solution for practical use.